SAOC 2020 and Other News

Symmetry Autumn of Code 2020

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The 3rd annual Symmetry Autumn of Code (SAoC) is on!

From now until August 16th, we’re accepting applications from motivated coders interested in getting paid to improve the D ecosystem. The SAoC committee will review all submissions and, based on the quality of the applications received, select a number of applicants to complete four milestones from September 15th to January 15th. Each participant will receive $1000 for the successful completion of each of the first three milestones, and one of them will receive an additional $1000 and a free trip (reimbursement for transportation and accommodation, and free registration) to the next real-world DConf (given the ongoing pandemic, we can’t yet be sure when that will be).

Anyone interested in programming D is welcome to apply, but preference will be given to those who can provide proof of enrollment in undergraduate or postgraduate university programs. For details on how to apply, see the SAoC 2020 page here at the D Blog.

The participants will need mentors, so we invite experienced D programmers interested in lending a hand to get in touch and to keep an eye out in the forums for any SAoC applicants in search of a mentor. As with the previous edition of SAoC, all mentors whose mentee completes the event will be guaranteed a one-time payment of $500 after the final milestone (mentors of unsuccessful mentees may still be eligible for the payment at the discretion of the SAoC committee). Potential mentors can follow the same link for details on their responsibilities and how to make themselves available.

We’re also looking for community input on potential SAoC projects. If there’s any work you’re aware of that needs doing in the D ecosystem and which may keep a lone coder occupied for 20 hours per week over four months, please let us know! Once again, details on how submit your suggestions and what sort of information we’re looking for can be found on the SAoC 2020 page.

Our SAoC 2019 selectee, Roberto Rosmaninho, was all set to attend DConf 2020 and we were all looking forward to meeting him. He’ll still be eligible to claim his free DConf trip at the next available opportunity.

SAoC would not be possible without the generosity of Symmetry Investments. A big thanks to them for once again funding this event and for the other ways, both financial and otherwise, they contribute back to the D programming language community.


Thanks to everyone who has shopped in the DLang Swag Emporium! To date, the D Language Foundation has received over $177 in royalties and referral fees. Thanks are also in order to those who have supported the foundation through Your purchases have brought over $288 into the General Fund. Amazon Smile is perhaps the easiest way to support D financially if you shop through Amazon’s .com domain (the D Language Foundation is unavailable in other Amazon domains). If you’ve never done so, you can select a charitable foundation (the D Language Foundation, of course) on your first visit to Then, every time you shop through that link, the foundation will receive a small percentage of your total purchase. Check your browser’s extension market for plugins that convert every link to a link!

On the Task Bounties front, we may have closed out a big bounty for bringing D to iOS and iPadOS, but there are still several other bounties waiting to be claimed. The latest, currently at $220, is a bounty to improve DLL support on Windows by closing two related Bugzilla issues; 50% of the total bounty will be paid for the successful closure (merged PR and DMD release) of each issue. We welcome anyone interested in fixing these issues to either up the bounty or roll up their sleeves and start working toward claiming it. If you’d like to contribute to multiple bounties with a single credit card payment, or seed one or more new bounties with a specific amount, visit the Task Bounty Catch-All and follow the instructions there.

Finally, the question was recently raised in the forums about how to view the D Language Foundation’s finances. Because the foundation is a 501(3)(c) non-profit public charity, the Form 990 that the organization is required to submit to the IRS every year is publicly available. There are different ways you can obtain the documents for multiple years, such as searching online databases or contacting the IRS directly. Several websites, such as, provide details on how to do so. The Form 990 does not break down specific expenditures or sources of income except for special circumstances (like scholarship payments). With Andrei’s help, I’m currently working on gathering up more information on the past five years of the foundation’s finances so that we can put up an overview page at It won’t be at line-item detail, but we hope to provide a little more detail than the Form 990. I can’t provide a timeline on when it will be available (I don’t consider it a high priority task, so I’m working on it sporadically), but expect it sometime in the next few months.

DConf Online?

Rumor has it that online conferences are actually a thing. Voices in the wind speak of the potential for an annual event related to D. I don’t usually listen to voices I hear in the wind, but this time I’m intrigued…

A Look at Chapel, D, and Julia Using Kernel Matrix Calculations


It seems each time you turn around there is a new programming language aimed at solving some specific problem set. Increased proliferation of programming languages and data are deeply connected in a fundamental way, and increasing demand for “data science” computing is a related phenomenon. In the field of scientific computing, Chapel, D, and Julia are highly relevant programming languages. They arise from different needs and are aimed at different problem sets: Chapel focuses on data parallelism on single multi-core machines and large clusters; D was initially developed as a more productive and safer alternative to C++; Julia was developed for technical and scientific computing and aimed at getting the best of both worlds—the high performance and safety of static programming languages and the flexibility of dynamic programming languages. However, they all emphasize performance as a feature. In this article, we look at how their performance varies over kernel matrix calculations and present approaches to performance optimization and other usability features of the languages.

Kernel matrix calculations form the basis of kernel methods in machine learning applications. They scale rather poorly—O(m n^2), where n is the number of items and m is the number of elements in each item. In our exercsie, m will be constant and we will be looking at execution time in each implementation as n increases. Here m = 784 and n = 1k, 5k, 10k, 20k, 30k, each calculation is run three times and an average is taken. We disallow any use of BLAS and only allow use of packages or modules from the standard library of each language, though in the case of D the benchmark is compared with calculations using Mir, a multidimensional array package, to make sure that my matrix implementation reflects the true performance of D. The details for the calculation of the kernel matrix and kernel functions are given here.

While preparing the code for this article, the Chapel, D, and Julia communities were very helpful and patient with all inquiries, so they are acknowledged here.

In terms of bias, going in I was much more familiar with D and Julia than I was with Chapel. However, getting the best performance from each language required a lot of interaction with each programming community, and I have done my best to be aware of my biases and correct for them where necessary.

Language Benchmarks for Kernel Matrix Calculation

The above chart (generated using R’s ggplot2 using a script) shows the performance benchmark time taken against the number of items n for Chapel, D, and Julia, for nine kernels. D performs best in five of the nine kernels, Julia performs best in two of the nine kernels, and in two of the kernels (Dot and Gaussian) the picture is mixed. Chapel was the slowest for all of the kernel functions examined.

It is worth noting that the mathematics functions used in D were pulled from C’s math API made available in D through its core.stdc.math module because the mathematical functions in D’s standard library std.math can be quite slow. The math functions used are given here. By way of comparison, consider the mathdemo.d script comparing the imported C log function D’s log function from std.math:

$ ldc2 -O --boundscheck=off --ffast-math --mcpu=native --boundscheck=off mathdemo.d && ./mathdemo
Time taken for c log: 0.324789 seconds.
Time taken for d log: 2.30737 seconds.

The Matrix object used in the D benchmark was implemented specifically because the use of modules outside standard language libraries was disallowed. To make sure that this implementation is competitive, i.e., it does not unfairly represent D’s performance, it is compared to Mir’s ndslice library written in D. The chart below shows matrix implementation times minus ndslice times; negative means that ndslice is slower, indicating that the implementation used here does not negatively represent D’s performance.


The code was run on a computer with an Ubuntu 20.04 OS, 32 GB memory, and an Intel® Core™ i9–8950HK CPU @ 2.90GHz with 6 cores and 12 threads.

$ julia --version
julia version 1.4.1
$ dmd --version
DMD64 D Compiler v2.090.1
ldc2 --version
LDC - the LLVM D compiler (1.18.0):
  based on DMD v2.088.1 and LLVM 9.0.0
$ chpl --version
chpl version 1.22.0



chpl script.chpl kernelmatrix.chpl --fast && ./script


ldc2 script.d kernelmatrix.d arrays.d -O5 --boundscheck=off --ffast-math -mcpu=native && ./script

Julia (no compilation required but can be run from the command line):

julia script.jl


Efforts were made to avoid non-standard libraries while implementing these kernel functions. The reasons for this are:

  • To make it easy for the reader after installing the language to copy and run the code. Having to install external libraries can be a bit of a “faff”.
  • Packages outside standard libraries can go extinct, so avoiding external libraries keeps the article and code relevant.
  • It’s completely transparent and shows how each language works.


Chapel uses a forall loop to parallelize over threads. Also, C pointers to each item are used rather than the default array notation, and guided iteration over indices is used:

proc calculateKernelMatrix(K, data: [?D] ?T)
  var n = D.dim(0).last;
  var p = D.dim(1).last;
  var E: domain(2) = {D.dim(0), D.dim(0)};
  var mat: [E] T;
  var rowPointers: [1..n] c_ptr(T) =
    forall i in 1..n do c_ptrTo(data[i, 1]);

  forall j in guided(1..n by -1) {
    for i in j..n {
      mat[i, j] = K.kernel(rowPointers[i], rowPointers[j], p);
      mat[j, i] = mat[i, j];
  return mat;

Chapel code was the most difficult to optimize for performance and required the highest number of code changes.


D uses a taskPool of threads from its std.parallel package to parallelize code. The D code underwent the fewest number of changes for performance optimization—a lot of the performance benefits came from the specific compiler used and the flags selected (discussed later). My implementation of a Matrix allows columns to be selected by reference via refColumnSelect.

auto calculateKernelMatrix(alias K, T)(K!(T) kernel, Matrix!(T) data)
  long n = data.ncol;
  auto mat = Matrix!(T)(n, n);

  foreach(j; taskPool.parallel(iota(n)))
    auto arrj = data.refColumnSelect(j).array;
    foreach(long i; j..n)
      mat[i, j] = kernel(data.refColumnSelect(i).array, arrj);
      mat[j, i] = mat[i, j];
  return mat;


The Julia code uses the @threads macro for parallelising the code and @views macro for referencing arrays. One confusing thing about Julia’s arrays is their reference status. Sometimes, as in this case, arrays will behave like value objects and they have to be referenced by using the @views macro, otherwise they generate copies. At other times they behave like reference objects, for example, when passing them into a function. It can be a little tricky dealing with this because you don’t always know what set of operations will generate a copy, but where this occurs @views provides a good solution.

The Symmetric type saves the small bit of extra work needed for allocating to both sides of the matrix.

function calculateKernelMatrix(Kernel::K, data::Array{T}) where {K <: AbstractKernel,T <: AbstractFloat}
  n = size(data)[2]
  mat = zeros(T, n, n)
  @threads for j in 1:n
      @views for i in j:n
          mat[i,j] = kernel(Kernel, data[:, i], data[:, j])
  return Symmetric(mat, :L)

The @bounds and @simd macros in the kernel functions were used to turn bounds checking off and apply SIMD optimization to the calculations:

struct DotProduct <: AbstractKernel end
@inline function kernel(K::DotProduct, x::AbstractArray{T, N}, y::AbstractArray{T, N}) where {T,N}
  ret = zero(T)
  m = length(x)
  @inbounds @simd for k in 1:m
      ret += x[k] * y[k]
  return ret

These optimizations are quite visible but very easy to apply.

Memory Usage

The total time for each benchmark and the total memory used was captured using the /usr/bin/time -v command. The output for each of the languages is given below.

Chapel took the longest total time but consumed the least amount of memory (nearly 6GB RAM peak memory):

Command being timed: "./script"
	User time (seconds): 113190.32
	System time (seconds): 6.57
	Percent of CPU this job got: 1196%
	Elapsed (wall clock) time (h:mm:ss or m:ss): 2:37:39
	Average shared text size (kbytes): 0
	Average unshared data size (kbytes): 0
	Average stack size (kbytes): 0
	Average total size (kbytes): 0
	Maximum resident set size (kbytes): 5761116
	Average resident set size (kbytes): 0
	Major (requiring I/O) page faults: 0
	Minor (reclaiming a frame) page faults: 1439306
	Voluntary context switches: 653
	Involuntary context switches: 1374820
	Swaps: 0
	File system inputs: 0
	File system outputs: 8
	Socket messages sent: 0
	Socket messages received: 0
	Signals delivered: 0
	Page size (bytes): 4096
	Exit status: 0

D consumed the highest amount of memory (around 20GB RAM peak memory) but took less total time than Chapel to execute:

Command being timed: "./script"
	User time (seconds): 106065.71
	System time (seconds): 58.56
	Percent of CPU this job got: 1191%
	Elapsed (wall clock) time (h:mm:ss or m:ss): 2:28:29
	Average shared text size (kbytes): 0
	Average unshared data size (kbytes): 0
	Average stack size (kbytes): 0
	Average total size (kbytes): 0
	Maximum resident set size (kbytes): 20578840
	Average resident set size (kbytes): 0
	Major (requiring I/O) page faults: 0
	Minor (reclaiming a frame) page faults: 18249033
	Voluntary context switches: 3833
	Involuntary context switches: 1782832
	Swaps: 0
	File system inputs: 0
	File system outputs: 8
	Socket messages sent: 0
	Socket messages received: 0
	Signals delivered: 0
	Page size (bytes): 4096
	Exit status: 0

Julia consumed a moderate amount of memory (around 7.5 GB peak memory) but ran the quickest—probably because its random number generator is the fastest:

Command being timed: "julia script.jl"
	User time (seconds): 49794.85
	System time (seconds): 30.58
	Percent of CPU this job got: 726%
	Elapsed (wall clock) time (h:mm:ss or m:ss): 1:54:18
	Average shared text size (kbytes): 0
	Average unshared data size (kbytes): 0
	Average stack size (kbytes): 0
	Average total size (kbytes): 0
	Maximum resident set size (kbytes): 7496184
	Average resident set size (kbytes): 0
	Major (requiring I/O) page faults: 794
	Minor (reclaiming a frame) page faults: 38019472
	Voluntary context switches: 2629
	Involuntary context switches: 523063
	Swaps: 0
	File system inputs: 368360
	File system outputs: 8
	Socket messages sent: 0
	Socket messages received: 0
	Signals delivered: 0
	Page size (bytes): 4096
	Exit status: 0

Performance optimization

The process of performance optimization in all three languages was very different, and all three communities were very helpful in the process. But there were some common themes.

  • Static dispatching of kernel functions instead of using polymorphism. This means that when passing the kernel function, use parametric (static compile time) polymorphism rather than runtime (dynamic) polymorphism where dispatch with virtual functions carries a performance penalty.
  • Using views/references rather than copying data over multiple threads makes a big difference.
  • Parallelising the calculations makes a huge difference.
  • Knowing if your array is row/column major and using that in your calculation makes a huge difference.
  • Bounds checks and compiler optimizations make a tremendous difference, especially in Chapel and D.
  • Enabling SIMD in D and Julia made a contribution to the performance. In D this was done using the -mcpu=native flag, and in Julia this was done using the @simd macro.

In terms of language-specific issues, getting to performant code in Chapel was the most challenging, and the Chapel code changed the most from easy-to-read array operations to using pointers and guided iterations. But on the compiler side it was relatively easy to add --fast and get a large performance boost.

The D code changed very little, and most of the performance was gained by the choice of compiler and its optimization flags. D’s LDC compiler is rich in terms of options for performance optimization. It has 8 -O optimization levels, but some are repetitions of others. For instance, -O, -O3, and -O5 are identical, and there are myriad other flags that affect performance in various ways. In this case the flags used were -O5 --boundscheck=off –ffast-math, representing aggressive compiler optimizations, bounds checking, and LLVM’s fast-math, and -mcpu=native to enable CPU vectorization instructions.

In Julia the macro changes discussed previously markedly improved the performance, but they were not too intrusive. I tried changing the optimization -O level, but this did not improve performance.

Quality of life

This section examines the relative pros and cons around the convenience and ease of use of each language. People underestimate the effort it takes to use a language day-to-day; the support and infrastructure required is significant, so it is worth comparing various facets of each language. Readers seeking to avoid the TLDR should scroll to the end of this section for the table comparing the language features discussed here. Every effort has been made to be as objective as possible, but comparing programming languages is difficult, bias prone, and contentious, so read this section with that in mind. Some elements looked at, such as arrays, are from the “data science”/technical/scientific computing point of view, and others are more general.


Programmers want a fast code/compile/result loop during development to quickly observe results and outputs in order to make progress or necessary changes. Julia’s interpreter is hands down the best for this and offers a smooth and feature-rich development experience, and D comes a close second. This code/compile/result loop in compilers can be slow even when compiling small code volumes. D has three compilers, the standard DMD compiler, the LLVM-based LDC compiler, and the GCC-based GDC. In this development process, the DMD and LDC compilers were used. DMD has very fast compilation times which is great for development. The LDC compiler is great at creating fast code. Chapel’s compiler is very slow in comparison. To give an example, running Linux’s time command on DMD vs Chapel’s compiler for the kernel matrix code with no optimizations gives us for D:

real	0m0.545s
user	0m0.447s
sys	0m0.101s

Compared with Chapel:

real	0m5.980s
user	0m5.787s
sys	0m0.206s

That’s a large actual and psychological difference, it can make programmers reluctant to check their work and delay the development loop if they have to wait for outputs, especially when source code increases in volume and compilation times become significant.

It is worth mentioning, however, that when developing packages in Julia, compilation times can be very long, and users have noticed that when they load some packages ,compilation times can stretch. So the experience of the development loop in Julia could vary, but in this specific case the process was seamless.

Documentation and examples

One way of comparing documentation in the different languages is to compare them all with Python’s official documentation, which is the gold standard for programming languages. It combines examples with formal definitions and tutorials in a seamless and user-friendly way. Since many programmers are familiar with the Python documentation, this approach gives an idea of how they compare.

Julia’s documentation is the closest to Python’s documentation quality and gives the user a very smooth, detailed, and relatively painless transition into the language. It also has a rich ecosystem of blogs, and topics on many aspects of the language are easy to come by. D’s official documentation is not as good and can be challenging and frustrating, however there is a very good free book “Programming in D” which is a great introduction to the language, but no single book can cover a programming language and there are not many sources for advanced topics. Chapel’s documentation is quite good for getting things done, though examples vary in presence and quality. Often, the programmer needs a lot of knowledge to look in the right place. A good topic for comparison is file I/O libraries in Chapel, D, and Julia. Chapel’s I/O library has too few examples but is relatively clear and straightforward; D’s I/O is kind of spread across a few modules, and documentation is more difficult to follow; Julia’s I/O documentation has lots of examples and is clear and easy to follow.

Perhaps one factor affecting Chapel’s adoption is lack of example—since its arrays have a non-standard interface, the user has to work hard to become familiar with them. Whereas even though D’s documentation may not be as good in places, the language has many similarities to C and C++, so it gets away with more sparse documentation.

Multi-dimensional Array support

“Arrays” here does not refer to native C and C++ style arrays available in D, but mathematical arrays. Julia and Chapel ship with array support and D does not, but it has the Mir library which has multidimensional arrays (ndslice). In the implementation of kernel matrix, I wrote my own matrix object in D, which is not difficult if you understand the principle, but it’s not something a user wants to do. However, D has a linear algebra library called Lubeck which has impressive performance characteristics and interfaces with all the usual BLAS implementations. Julia’s arrays are by far the easiest and most familiar. Chapel’s arrays are more difficult to get started with than Julia’s but are designed to be run on single-core, multi-core, and computer clusters using the same or very similar code, which is a good unique selling point.

Language power

Since Julia is a dynamic programming language, some might say, “well Julia is a dynamic language which is far more permissive than static programming languages, therefore the debate is over”, but it’s more complicated than that. There is power in static type systems. Julia has a type system similar in nature to type systems from static languages, so you can write code as if you were using a static language, but you can do things reserved only for dynamic languages. It has a highly developed generic and meta-programming syntax and powerful macros. It also has a highly flexible object system and multiple dispatch. This mix of features is what makes Julia the most powerful language of the three.

D was intended to be a replacement for C++ and takes very much after C++ (and also borrows from Java), but makes template programming and compile-time evaluation much more user-friendly than in C++. It is a single dispatch language (though multi-methods are available in a package). Instead of macros, D has string and template “mixins” which serve a similar purpose.

Chapel has generic programming support and nascent support for single dispatch OOP, no macro support, and is not yet as mature as D or Julia in these terms.

Concurrency & Parallel Programming

Nowadays, new languages tout support for concurrency and its popular subset, parallelism, but the details vary a lot between languages. Parallelism is more relevant in this example and all three languages deliver. Writing parallel for loops is straightforward in all three languages.

Chapel’s concurrency model has much more emphasis on data parallelism but has tools for task parallelism and ships with support for cluster-based concurrency.

Julia has good support for both concurrency and parallelism.

D has industry strength support for parallelism and concurrency, though its support for threading is much less well documented with examples.

Standard Library

How good is the standard library of all three languages in general? What range of tasks do they allow users to easily tend to? It’s a tough question because library quality and documentation factor in. All three languages have very good standard libraries. D has the most comprehensive standard library, but Julia is a great second, then Chapel, but things are never that simple. For example, a user seeking to write binary I/O may find Julia the easiest to start with; it has the most straightforward, clear interface and documentation, followed by Chapel, and then D. Though in my implementation of an IDX file reader, D’s I/O was the fastest, but then Julia code was easy to write for cases unavailable in the other two languages.

Package Managers & Package Ecosystems

In terms of documentation, usage, and features, D’s Dub package manager is the most comprehensive. D also has a rich package ecosystem in the Dub website, Julia’s package manager runs tightly integrated with GitHub and is a good package system with good documentation. Chapel has a package manager but does not have a highly developed package ecosystem.

C Integration

C interop is easy in all three languages; Chapel has good documentation but is not as well popularised as the others. D’s documentation is better and Julia’s documentation is the most comprehensive. Oddly enough though, none of the languages’ documentation show the commands required to compile your own C code and integrate it with the language, which is an oversight especially when it comes to novices. It is, however, easy to search for and find examples for the compilation process in D and Julia.


All three languages have convenient places where users can ask questions. For Chapel, the easiest place is Gitter, for Julia it’s Discourse (though there is a Julia Gitter), and for D it’s the official website forum. The Julia community is the most active, followed by D, and then Chapel. I’ve found that you’ll get good responses from all three communities, but you’ll probably get quicker answers from the D and Julia communities.

Chapel D Julia
Compilation/Interactivty Slow Fast Best
Documentation & Examples Detailed Patchy Best
Multi-dimensional Arrays Yes Native Only
(library support)
Language Power Good Great Best
Concurrency & Parallelism Great Great Good
Standard Library Good Great Great
Package Manager & Ecosystem Nascent Best Great
C Integration Great Great Great
Community Small Vibrant Largest

Table for quality of life features in Chapel, D & Julia


If you are a novice programmer writing numerical algorithms and doing calculations based in scientific computing and want a fast language that’s easy to use, Julia is your best bet. If you are an experienced programmer working in the same space, Julia is still a great option. If you specifically want a more conventional, “industrial strength”, statically compiled, high-performance language with all the “bells and whistles”, but want something more productive, safer, and less painful than C++, then D is your best bet. You can write “anything” in D and get great performance from its compilers. If you need to get array calculations happening on clusters, then Chapel is probably the easiest place to go.

In terms of raw performance on this task, D was the winner, clearly performing better in 5 out of the 9 kernels benchmarked. This exercise reveals that Julia’s label as a high-performance language is more than just hype—it has held it’s own against highly competitive languages. It was harder than expected to get competitive performance from Chapel—it took a lot of investigation from the Chapel team to come up with the current solution. However, as the Chapel language matures we could see further improvement.

Lomuto’s Comeback

The Continental Club in Austin, Texas, USA
Sunday, January 5, 1987

“Thank you for your kind invitation, Mr. Lomuto. I will soon return to England so this is quite timely.”

“And thanks for agreeing to meeting me, Mister… Sir… Charles… A.R… Hoare. It’s a great honor. I don’t even know how to address you. Were you knighted?”

“Call me Tony, and if it’s not too much imposition please allow me to call you Nico.”

On the surface, a banal scene—two men enjoying a whiskey. However, a closer look revealed a number of intriguing details. For starters, a tension you could cut with a knife.

Dressed in a perfectly tailored four-piece suit worn with the nonchalance only an Englishman could pull off, Tony Hoare was as British as a cup of tea. His resigned grimaces as he was sipping from his glass spoke volumes about his opinion of Bourbon versus Scotch. On the other side of the small table, Nico Lomuto couldn’t have been more different: a casually dressed coder enjoying his whiskey with Coca-Cola (a matter so outrageous that Tony had decided early on to studiously pretend not to notice, as he would when confronted with ripe body odor or an offensive tattoo), in a sort of relaxed awe at the sight of the Computer Science giant he had just met.

“Listen, Tony,” Nico said as the chit chat petered off, “about that partitioning algorithm. I never meant to publish or—”

“Oh? Yes, yes, the partitioning algorithm.” Tony’s eyebrows rose with feigned surprise, as if it had escaped his mind that every paper and book on quicksort in the past five years mentioned their names together. It was obviously the one thing connecting the two men and the motivation of the meeting, but Tony, the perfect gentleman, could talk about the weather for hours with a pink elephant in the room if his conversation partner didn’t bring it up.

“Yeah, that partitioning algorithm that keeps on getting mentioned together with yours,” Nico continued. “I’m not much of an algorithms theorist. I’m working on Ada, and this entire thing about my partition scheme is a distraction. The bothersome part about it”—Nico was speaking in the forthcoming tone of a man with nothing to hide—”is that it’s not even a better algorithm. My partitioning scheme will always do the same number of comparisons and at least as many swaps as yours. In the worst case, mine does n additional swaps—n! I can’t understand why they keep on mentioning the blessed thing. It’s out of my hands now. I can’t tell them what algorithms to teach and publish. It’s like bubblesort. Whenever anyone mentions quicksort, there’s some chowderhead—or should I say bubblehead—in the audience going, yes, I also heard of the bubblesort algorithm. Makes my blood curdle.”

Nico sighed. Tony nodded. Mutual values. Rapport filled the air in between as suddenly, quietly, and pleasantly as the smell of cookies out of the oven. A few seconds went by. Jack and Coke sip. On the other side of the table, Bourbon sip, resigned grimace.

Tony spoke with the carefully chosen words of a scientist who wants to leave no hypothesis unexplored. “I understand, Nico. Yet please consider the following. Your algorithm is simple and regular, moves in only one direction, and does at most one swap per step. That may be appropriate for some future machines that…”

“No matter the machine, more swaps can’t be better than fewer swaps. It’s common sense,” Nico said, peremptorily.

“I would not be so sure. Computers do not have common sense. Computers are surprising. It stands to reason they’ll continue to be. Well, how about we enjoy this evening. Nothing like a good conversation in a quiet club.”

“Yeah. Cheers. This is a fun place. I hear they’ll have live country music soon.”

“Charming.” Somewhat to his own surprise, Tony mustered a polite smile.

Chestnut Hill, Massachusetts, USA
Present Day

I’ve carried an unconfessed addiction to the sorting problem for many years. Wasn’t that difficult to hide—to a good extent, an obsessive inclination to studying sorting is a socially tolerated déformation professionnelle; no doubt many a programmer has spent a few late nights trying yet another sorting idea that’s going to be so much better than the others. So nobody raised an eyebrow when I wrote about sorting all the way back in 2002 (ever heard about “fit pivot?” Of course you didn’t). There was no intervention organized when I wrote D’s std.sort, which turned out to be sometimes quadratic (and has been thankfully fixed since). No scorn even when I wrote an academic paper on the selection problem (sort’s cousin) as an unaffiliated outsider, which even the conference organizers said was quite a trick. And no public outrage when I spoke about sorting at CppCon 2019. Coders understand.

So, I manage. You know what they say—one day at a time. Yet I did feel a tinge of excitement when I saw the title of a recent paper: “Branch Mispredictions Don’t Affect Mergesort.” Such an intriguing title. To start with, are branch mispredictions expected to affect mergesort? I don’t have much of an idea, mainly because everybody and their cat is using quicksort, not mergesort, so the latter hasn’t really been at the center of my focus. But hey, I don’t even need to worry about it because the title resolutely asserts that that problem I didn’t know I was supposed to worry about, I don’t need to worry about after all. So in a way the title cancels itself out. Yet I did read the paper (and recommend you do the same) and among many interesting insights, there was one that caught my attention: Lomuto’s partitioning scheme was discussed as a serious contender (against the universally-used Hoare partition) from an efficiency perspective. Efficiency!

It turns out modern computing architecture does, sometimes, violate common sense.

To Partition, Perchance to Sort

Let’s first recap the two partitioning schemes. Given an array and a pivot element, to partition the array means to arrange elements of the array such that all elements smaller than or equal to the pivot are on the left, and elements greater than or equal to the pivot are on the right. The final position of the pivot would be at the border. (If there are several equivalent pivot values that final pivot position may vary, with important practical consequences; for this discussion, however, we can assume that all array values are distinct.)

Lomuto’s partitioning scheme walks the array left to right maintaining a “read” position and a “write” position, both initially at 0. For each element read, if the value seen by the “read head” is greater than the pivot, it gets skipped (with the read head moving to the next position). Otherwise, the value at the read head is swapped with that at the write head, and both heads advance by one position. When the read head is done, the position of the write head defines the partition. Refer to the nice animation below (from Wikipedia user Mastremo, used unmodified under the CC-BY-SA 3.0 license).

The problem with Lomuto’s partition is that it may do unnecessary swaps. Consider the extreme case of an array with only the leftmost element greater than the pivot. That element will be awkwardly moved to the right one position per iteration step, in a manner not unlike, um, bubblesort.

Hoare’s partitioning scheme elegantly solves that issue by iterating concomitantly from both ends of the array with two “read/write heads”. They skip elements that are already appropriately placed (less than the pivot on the left, greater than the pivot on the right), and swap only one smaller element from the left with one greater element from the right. When the two heads meet, the array is partitioned around the meet point. The extreme case described above is handled with a single swap. Most contemporary implementations of quicksort use Hoare partition, for obvious reasons: it does as many comparisons as the Lomuto partition and fewer swaps.

Given that Hoare partition clearly does less work than Lomuto partition, the question would be why ever teach or use the latter at all. Alexander Stepanov, the creator of the STL, authored a great discussion about partitioning and makes a genericity argument: Lomuto partition only needs forward iterators, whereas Hoare partition requires bidirectional iterators. That’s a valuable insight, albeit of limited practical utility: yes, you could use Lomuto’s partition on singly-linked lists, but most of the time you partition for quicksort’s sake, and you don’t want to quicksort singly-linked lists; mergesort would be the algorithm of choice.

Yet a very practical—and very surprising—argument does exist, and is the punchline of this article: implemented in a branch-free manner, Lomuto partition is a lot faster than Hoare partition on random data. Given that quicksort spends most of its time partitioning, it follows that we are looking at a hefty improvement of quicksort (yes, I am talking about industrial strength implementations for C++ and D) by replacing its partitioning algorithm with one that literally does more work.

You read that right.

Time to Spin Some Code

To see how the cookie crumbles, let’s take a look at a careful implementation of Hoare partition. To eliminate all extraneous details, the code in this article is written for long as the element type and uses raw pointers. It compiles and runs the same with a C++ or D compiler. This article will carry along implementations of all routines in both languages because much research literature measures algorithm performance using C++’s std::sort as an important baseline.

Partition using the minimum of the first and last element as pivot.
Returns: a pointer to the final position of the pivot.
long* hoare_partition(long* first, long* last) {
    assert(first <= last);
    if (last - first < 2)
        return first; // nothing interesting to do
    if (*first > *last)
        swap(*first, *last);
    auto pivot_pos = first;
    auto pivot = *pivot_pos;
    for (;;) {
        auto f = *first;
        while (f < pivot)
            f = *++first;
        auto l = *last;
        while (pivot < l)
            l = *--last;
        if (first >= last)
        *first = l;
        *last = f;
    swap(*first, *pivot_pos);
    return first;

(You may find the choice of pivot a bit odd, but not to worry: usually it’s a more sophisticated scheme—such as median-of-3—but what’s important to the core loop is that the pivot is not the largest element of the array. That allows the core loop to omit a number of limit conditions without running off array bounds.)

There are a lot of good things to say about the efficiency of this implementation (which you’re likely to find, with minor details changed, in implementations of the C++ or D standard library). You could tell the code above was written by people who live trim lives. People who keep their nails clean, show up when they say they’ll show up, and call Mom regularly. They do a wushu routine every morning and don’t let computer cycles go to waste. That code has no slack in it. The generated Intel assembly is remarkably tight and virtually identical for C++ and D. It only varies across backends, with LLVM at a slight code size advantage (see clang and ldc) over gcc (see g++ and gdc).

The initial implementation of Lomuto’s partition shown below works well for exposition, but is sloppy from an efficiency perspective:

Choose the pivot as the first element, then partition.
Returns: a pointer to the final position of the pivot. 
long* lomuto_partition_naive(long* first, long* last) {
    assert(first <= last);
    if (last - first < 2)
        return first; // nothing interesting to do
    auto pivot_pos = first;
    auto pivot = *first;
    for (auto read = first; read < last; ++read) {
        if (*read < pivot) {
            swap(*read, *first);
    swap(*first, *pivot_pos);
    return first;

For starters, the code above will do a lot of silly no-op swaps (array element with itself) if a bunch of elements on the left of the array are greater than the pivot. All that time first==write, so swapping *first with *write is unnecessary and wasteful. Let’s fix that with a pre-processing loop that skips the uninteresting initial portion:

Partition using the minimum of the first and last element as pivot. 
Returns: a pointer to the final position of the pivot.
long* lomuto_partition(long* first, long* last) {
    assert(first <= last);
    if (last - first < 2)
        return first; // nothing interesting to do
    if (*first > *last)
        swap(*first, *last);
    auto pivot_pos = first;
    auto pivot = *first;
    // Prelude: position first (the write head) on the first element
    // larger than the pivot.
    do {
    } while (*first < pivot);
    assert(first <= last);
    // Main course.
    for (auto read = first + 1; read < last; ++read) {
        auto x = *read;
        if (x < pivot) {
            *read = *first;
            *first = x;
    // Put the pivot where it belongs.
    assert(*first >= pivot);
    *pivot_pos = *first;
    *first = pivot;
    return first;

The function now chooses the pivot as the smallest of first and last element, just like hoare_partition. I also made another small change—instead of using the swap routine, let’s use explicit assignments. The optimizer takes care of that automatically (enregistering plus register allocation for the win), but expressing it in source helps us see the relatively expensive array reads and array writes. Now for the interesting part. Let’s focus on the core loop:

for (auto read = first + 1; read < last; ++read) {
    auto x = *read;
    if (x < pivot) {
        *read = *first;
        *first = x;

Let’s think statistics. There are two conditionals in this loop: read < last and x < pivot. How predictable are they? Well, the first one is eminently predictable—you can reliably predict it will always be true, and you’ll only be wrong once no matter how large the array is. Compiler writers and hardware designers know this, and design the fastest path under the assumption loops will continue. (Gift idea for your Intel engineer friend: a doormat that reads “The Backward Branch Is Always Taken.”) The CPU will speculatively start executing the next iteration of the loop even before having decided whether the loop should continue. That work will be thrown away only once, at the end of the loop. That’s the magic of speculative execution.

Things are quite a bit less pleasant with the second test, x < pivot. If you assume random data and a randomly-chosen pivot, it could go either way with equal probability. That means speculative execution is not effective at all, which is very bad for efficiency. How bad? In a deeply pipelined architecture (as all are today), failed speculation means the work done by several pipeline stages needs to be thrown away, which in turn propagates a bubble of uselessness through the pipeline (think air bubbles in a garden hose). If these bubbles occur too frequently, the loop produces results at only a fraction of the attainable bandwidth. As the measurements section will show, that one wasted speculation takes away about 30% of the potential speed.

How to improve on this problem? Here’s an idea: instead of making decisions that control the flow of execution, we write the code in a straight-line manner and we incorporate the decisions as integers that guide the data flow by means of carefully chosen array indexing. Be prepared—this will force us to do silly things. For example, instead of doing two conditional writes per iteration, we’ll do exactly two writes per iteration no matter what. If the writes were not needed, we’ll overwrite words in memory with their own value. (Did I mention “silly things”?) To prepare the code for all that, let’s rewrite it as follows:

for (auto read = first + 1; read < last; ++read) {
    auto x = *read;
    if (x < pivot) {
        *read = *first;
        *first = x;
        first += 1; 
    } else {
        *read = x;
        *first = *first;
        first += 0; 

Now the two branches of the loop are almost identical save for the data. The code is still correct (albeit odd) because on the else branch it needlessly writes *read over itself and *first over itself. How do we now unify the two branches? Doing so in an efficient manner takes a bit of pondering and experimentation. Conditionally incrementing first is easy because we can always write first += x < pivot. Piece of cake. The two memory writes are more difficult, but the basic idea is to take the difference between pointers and use indexing. Here’s the code. Take a minute to think it over:

for (; read < last; ++read) {
    auto x = *read;
    auto smaller = -int(x < pivot);
    auto delta = smaller & (read - first);
    first[delta] = *first;
    read[-delta] = x;
    first -= smaller;

To paraphrase a famous Latin aphorism, explanatio longa, codex brevis est. Short is the code, long is the ‘splanation. The initialization of smaller with -int(x < pivot) looks odd but has a good reason: smaller can serve as both an integral (0 or -1) used with the usual arithmetic and also as a mask that is 0 or 0xFFFFFFFF (i.e. bits set all to 0 or all to 1) used with bitwise operations. We will use that mask to allow or obliterate another integral in the next line that computes delta. If x < pivotsmaller is all ones and delta gets initialized to read - first. Subsequently, delta is used in first[delta] and read[-delta], which really are syntactic sugar for *(first + delta) and *(read - delta), respectively. If we substitute delta in those expressions, we obtain *(first + (read - first)) and *(read - (read - first)), respectively.

The last line, first -= smaller, is trivial: if x < pivot, subtract -1 from first, which is the same as incrementing first. Otherwise, subtract 0 from first, effectively leaving first alone. Nicely done.

With x < pivot substituted to 1, the calculation done in the body of the loop becomes:

auto x = *read;
int smaller = -1;
auto delta = -1 & (read - first);
*(first + (read - first)) = *first;
*(read - (read - first)) = x;
first -= -1;

Kind of magically the two pointer expressions simplify down to *read and *first, so the two assignments effect a swap (recall that x had been just initialized with *read). Exactly what we did in the true branch of the test in the initial version!

If x < pivot is false, delta gets initialized to zero and the loop body works as follows:

auto x = *read;
int smaller = 0;
auto delta = 0 & (read - first);
*(first + 0) = *first;
*(read - 0) = x;
first -= 0;

This time things are simpler: *first gets written over itself, *read also gets written over itself, and first is left alone. The code has no effect whatsoever, which is exactly what we wanted.

Let’s now take a look at the entire function:

long* lomuto_partition_branchfree(long* first, long* last) {
    assert(first <= last);
    if (last - first < 2)
        return first; // nothing interesting to do
    if (*first > *last)
        swap(*first, *last);
    auto pivot_pos = first;
    auto pivot = *first;
    do {
        assert(first <= last);
    } while (*first < pivot);
    for (auto read = first + 1; read < last; ++read) {
        auto x = *read;
        auto smaller = -int(x < pivot);
        auto delta = smaller & (read - first);
        first[delta] = *first;
        read[-delta] = x;
        first -= smaller;
    assert(*first >= pivot);
    *pivot_pos = *first;
    *first = pivot;
    return first;

A beaut, isn’t she? Even more beautiful is the generated code—take a look at clang/ldc and g++/gdc. Again, there is a bit of variation across backends.

Experiments and Results

All code is available at

To draw a fair comparison between the two partitioning schemes, I’ve put together a quicksort implementation. This is because most often a partition would be used during quicksort. For the sake of simplification, the test implementation omits a few details present in industrial quicksort implementations, which need to worry about a variety of data shapes (partially presorted ascending or descending, with local patterns, with many duplicates, etc). Library implementations choose the pivot carefully from a sample of usually 3-9 elements, possibly with randomization, and have means to detect and avoid pathological inputs, most often by using Introsort.

In our benchmark, for simplicity, we only test against random data, and the choice of pivot is simply the minimum of first and last element. This is without loss of generality; better pivot choices and adding introspection are done the same way regardless of the partitioning method. Here, the focus is to compare the performance of Hoare vs. Lomuto vs. branch-free Lomuto.

The charts below plot the time taken by one sorting operation depending on the input size. The machine used is an Intel i7-4790 at 3600 MHz with a 256KB/1MB/8MB cache hierarchy running Ubuntu 20.04. All builds were for maximum speed (-O3, no assertions, no boundcheck for the D language). The input is a pseudorandom permutation of longs with the same seed for all languages and platforms. To eliminate noise, the minimum is taken across several epochs.

The results for the D language are shown below, including the standard library’s std.sort as a baseline.

The results for C++ are shown in the plots below. Again the standard library implementation std::sort is included as a baseline.

One important measurement is the CPU utilization efficiency, shown by Intel VTune as “the micropipe”, a diagram illustrating inefficiencies in resource utilization. VTune’s reports are very detailed but the micropipe gives a quick notion of where the work goes. To interpret a micropipe, think of it as a funnel. The narrower the exit (on the right), the slower the actual execution as a fraction of potential speed.

The micropipes shown below correspond to the Hoare partition, Lomuto partition (in the traditional implementation), and branch-free Lomuto partition. The first two throw away about 30% of all work as bad speculation. In contrast, the Lomuto branchless partition wastes no work on speculation, which allows it a better efficiency in spite of more memory writes.

Intel VTune pipe efficiency diagram for the Hoare partition. A large percentage of work is wasted on failed speculation.

Intel VTune pipe efficiency diagram for the traditional “branchy” Lomuto partition, featuring about as much failed speculation as the Hoare partition.

Intel VTune pipe efficiency diagram for the Lomuto branch-free partition. Virtually no work is wasted on failed speculation, leading to a much better efficiency.


The four versions (two languages times two backends) exhibit slight variations due to differences in standard library implementations and backend versions. It is not surprising that minute variations in generated code are liable to create measurable differences in execution speed.

As expected, the “branchy” Lomuto partition compares poorly with Hoare partition, especially for large input sizes. Both are within the same league as the standard library implementation of the sort routine. Sorting using the branchless Lomuto partition, however, is the clear winner by a large margin regardless of platform, backend, and input size.

It has become increasingly clear during the past few years that algorithm analysis—and proposals for improvements derived from it—cannot be done solely with pen and paper using stylized computer architectures with simplistic cost models. The efficiency of sorting is determined by a lot more than counting the comparisons and swaps—at least, it seems, the predictability of comparisons must be taken into account. In the future, I am hopeful that better models of computation will allow researchers to rein in the complexity. For the time being, it seems, algorithm optimization remains hopelessly experimental.

For sorting in particular, Lomuto is definitely back and should be considered by industrial implementations of quicksort on architectures with speculative execution.


Many thanks are due to Amr Elmasry, Jyrki Katajainen, and Max Stenmark for an inspirational paper. I haven’t yet been able to engineer a mergesort implementation (the main result of their paper) that beats the best quicksort described here, but I’m working on it. (Sorry, Sorters Anonymous… I’m still off the wagon.) I’d like to thank to Michael Parker and the commentators at the end of this post for fixing many of my non-native-speaker-isms. (Why do they say “pretend not to notice” and “pretend to not notice”? I never remember the right one.) Of course, most of the credit is due to Nico Lomuto, who defined an algorithm that hasn’t just stood the test of time—it passed it.

Interfacing D with C: Arrays and Functions (Arrays Part 2)

Digital Mars D logo

This post is part of an ongoing series on working with both D and C in the same project. The previous post explored the differences in array declaration and initialization. This post takes the next step: declaring and calling C functions that take arrays as parameters.

Arrays and C function declarations

Using C libraries in D is extremely easy. Most of the time, things work exactly as one would expect, but as we saw in the previous article there can be subtle differences. When working with C functions that expect arrays, it’s necessary to fully understand these differences.

The most straightforward and common way of declaring a C function that accepts an array as a parameter is to to use a pointer in the parameter list. For example, this hypothetical C function:

void f0(int *arr);

In C, any array of int can be passed to this function no matter how it was declared. Given int a[], int b[3], or int *c, the function calls f0(a), f0(b), and f0(c) are all the same: a pointer to the first element of each array is passed to the function. Or using the lingo of C programmers, arrays decay to pointers

Typically, in a function like f0, the implementer will expect the array to have been terminated with a marker appropriate to the context. For example, strings in C are arrays of char that are terminated with the \0 character (we’ll look at D strings vs. C strings in a future post). This is necessary because, without that character, the implementation of f0 has no way to know which element in the array is the last one. Sometimes, a function is simply documented to expect a certain length, either in comments or in the function name, e.g., a vector3f_add(float *vec) will expect that vec points to exactly 3 elements. Another option is to require the length of the array as a separate argument:

void f1(int *arr, size_t len);

None of these approaches is foolproof. If f0 receives an array with no end marker or which is shorter than documented, or if f1 receives an array with an actual length shorter than len, then the door is open for memory corruption. D arrays take this possibility into account, making it much easier to avoid such problems. But again, even D’s safety features aren’t 100% foolproof when calling C functions from D.

There are other, less common, ways array parameters may be declared in C:

void f2(int arr[]);
void f3(int arr[9]);
void f4(int arr[static 9]);

Although these parameters are declared using C’s array syntax, they boil down to the exact same function signature as f0 because of the aforementioned pointer decay. The [9] in f3 triggers no special enforcement by the compiler; arr is still effectively a pointer to int with unknown length. The [9] serves as documentation of what the function expects, and the implementation cannot rely on the array having nine elements.

The only potential difference is in f4. The static added to the declaration tells the compiler that the function must take an array of, in this case, at least nine elements. It could have more than nine, but it can’t have fewer. That also rules out null pointers. The problem is, this isn’t necessarily enforced. Depending on which C compiler you use, if you shortchange the function and send it less than nine elements you might see warnings if they are enabled, but the compiler might not complain at all. (I haven’t tested current compilers for this article to see if any are actually reporting errors for this, or which ones provide warnings.)

The behavior of C compilers doesn’t matter from the D side. All we need be concerned with is declaring these functions appropriately so that we can call them from D such that there are no crashes or unexpected results. Because they are all effectively the same, we could declare them all in D like so:

void f0(int* arr);
void f1(int* arr, size_t len);
void f2(int* arr);
void f3(int* arr);
void f4(int* arr);

But just because we can do a thing doesn’t mean we should. Consider these alternative declarations of f2, f3, and f4:

void f2(int[] arr);
void f3(int[9] arr);
void f4(int[9] arr);

Are there any consequences of taking this approach? The answer is yes, but that doesn’t mean we should default to int* in each case. To understand why, we need first to explore the innards of D arrays.

The anatomy of a D array

The previous article showed that D makes a distinction between dynamic and static arrays:

int[] a0;
int[9] a1;

a0 is a dynamic array and a1 is a static array. Both have the properties .ptr and .length. Both may be indexed using the same syntax. But there are some key differences between them.

Dynamic arrays

Dynamic arrays are usually allocated on the heap (though that isn’t a requirement). In the above case, no memory for a0 has been allocated. It would need to be initialized with memory allocated via new or malloc, or some other allocator, or with an array literal. Because a0 is uninitialized, a0.ptr is null and a0.length is 0.

A dynamic array in D is an aggregate type that contains the two properties as members. Something like this:

struct DynamicArray {
    size_t length;
    size_t ptr;

In other words, a dynamic array is essentially a reference type, with the pointer/length pair serving as a handle that refers to the elements in the memory address contained in the ptr member. Every built-in D type has a .sizeof property, so if we take a0.sizeof, we’ll find it to be 8 on 32-bit systems, where size_t is a 4-byte uint, and 16 on 64-bit systems, where size_t is an 8-byte ulong. In short, it’s the size of the handle and not the cumulative size of the array elements.

Static arrays

Static arrays are generally allocated on the stack. In the declaration of a1, stack space is allocated for nine int values, all of which are initialized to int.init (which is 0) by default. Because a1 is initialized, a1.ptr points to the allocated space and a1.length is 9. Although these two properties are the same as those of the dynamic array, the implementation details differ.

A static array is a value type, with the value being all of its elements. So given the declaration of a1 above, its nine int elements indicate that a1.sizeof is 9 * int.sizeof, or 36. The .length property is a compile-time constant that never changes, and the .ptr property, though not readable at compile time, is also a constant that never changes (it’s not even an lvalue, which means it’s impossible to make it point somewhere else).

These implementation details are why we must pay attention when we cut and paste C array declarations into D source modules.

Passing D arrays to C

Let’s go back to the declaration of f2 in C and give it an implementation:

void f2(int arr[]) {
    for(int i=0; i<3; ++i)
        printf("%d\n", arr[i]);

A naïve declaration in D:

extern(C) void f2(int[]);

void main() {
    int[] a = [10, 20, 30];

I say naïve because this is never the right answer. Compiling f2.c with df2.d on Windows (cl /c f2.c in the “x64 Native Tools” command prompt for Visual Studio, followed by dmd -m64 df2.d f2.obj), then running df2.exe, shows me the following output:


There is no compiler error because the declaration of f2 is pefectly valid D. The extern(C) indicates that this function uses the cdecl calling convention. Calling conventions affect the way arguments are passed to functions and how the function’s symbol is mangled. In this case, the symbol will be either _f2 or f2 (other calling conventions, like stdcallextern(Windows) in D—have different mangling schemes). The declaration still has to be valid D. (In fact, any D function can be marked as extern(C), something which is necessary when creating a D library that will be called from other languages.)

There is also no linker error. DMD is calling out to the system linker (in this case, Microsoft’s link.exe), the same linker used by the system’s C and C++ compilers. That means the linker has no special knowledge about D functions. All it knows is that there is a call to a symbol, f2 or _f2, that needs to be linked with the implementation. Since the type and number of parameters are not mangled into the symbol name, the linker will happily link with any matching symbol it finds (which, by the way, is the same thing it would do if a C program tried to call a C function which was declared with an incorrect parameter list).

The C function is expecting a single pointer as an argument, but it’s instead receiving two values: the array length followed by the array pointer.

The moral of this story is that any C function with array parameters declared using array syntax, like int[], should be declared to accept pointers in D. Change the D source to the following and recompile using the same command line as before (there’s no need to recompile the C file):

extern(C) void f2(int*);

void main() {
    int[] a = [10, 20, 30];

Note the use of a.ptr. It’s an error to try to pass a D array argument where a pointer is expected (with one very special exception, string literals, which I’ll cover in the next article in this series), so the array’s .ptr property must be used instead.

The story for f3 and f4 is similar:

void f3(int arr[9]);
void f4(int arr[static 9]);

Remember, int[9] in D is a static array, not a dynamic array. The following do not match the C declarations:

void f3(int[9]);
void f4(int[9]);

Try it yourself. The C implementation:

void f3(int arr[9]) {
    for(int i=0; i<9; ++i)
        printf("%d\n", arr[i]);

And the D implementation:

extern(C) void f3(int[9]);

void main() {
    int[9] a = [10, 20, 30, 40, 50, 60, 70, 80, 90];

This is likely to crash, depending on the system. Rather than passing a pointer to the array, this code is instead passing all nine array elements by value! Now consider a C library that does something like this:

typedef float[16] mat4f;
void do_stuff(mat4f mat);

Generally, when writing D bindings to C libraries, it’s a good idea to keep the same interface as the C library. But if the above is translated like the following in D:

alias mat4f = float[16];
extern(C) void do_stuff(mat4f);

The sixteen floats will be passed to do_stuff every time it’s called. The same for all functions that take a mat4f parameter. One solution is just to do the same as in the int[] case and declare the function to take a pointer. However, that’s no better than C, as it allows the function to be called with an array that has fewer elements than expected. We can’t do anything about that in the int[] case, but that will usually be accompanied by a length parameter on the C side anyway. C functions that take typedef’d types like mat4f usually don’t have a length parameter and rely on the caller to get it right.

In D, we can do better:

void do_stuff(ref mat4f);

Not only does this match the API implementor’s intent, the compiler will guarantee that any arrays passed to do_stuff are static float arrays with 16 elements. Since a ref parameter is just a pointer under the hood, all is as it should be on the C side.

With that, we can rewrite the f3 example:

extern(C) void f3(ref int[9]);

void main() {
    int[9] a = [10, 20, 30, 40, 50, 60, 70, 80, 90];


Most of the time, when interfacing with C from D, the C API declarations and any example code can be copied verbatim in D. But most of the time is not all of the time, so care must be taken to account for those exceptional cases. As we saw in the previous article, carelessness when declaring array variables can usually be caught by the compiler. As this article shows, the same is not the case for C function declarations. Interfacing D with C requires the same care as when writing C code.

In the next article in this series, we’ll look at mixing D strings and C strings in the same program and some of the pitfalls that may arise. In the meantime, Steven Schveighoffer’s excellent article, “D Slices”, is a great place to start for more details about D arrays.

Thanks to Walter Bright and Átila Neves for their valuable feedback on this article.

DustMite: The General-Purpose Data Reduction Tool

If you’ve been around for a while, or are a particularly adventurous developer who enjoys mixing language features in interesting ways, you may have run into one compiler bug or two:

Implementation bugs are inevitably a part of using cutting-edge programming languages. Should you run into one, the steps to proceed are generally as follows:

  1. Reduce the failing program to a minimal, self-contained example.
  2. Add a description of what happens and what you expect to happen.
  3. Post it on the bug tracker.

Nine years ago, an observation was made that when filing and fixing compiler bugs, a disproportionate amount of time was spent on the first step. When your program stops compiling “out of the blue”, or when the bug stops reproducing after the code is taken out of its context, manually paring down a large codebase by repeatedly cutting out code and checking if the bug still occurs becomes a tedious and repetitive task.

Fortunately, tedious and repetitive tasks are what computers are good for; they just have to be tricked into doing them, usually by writing a program. Enter DustMite.

The first version.

The basic operation is simple. The tool takes as inputs:

  • a data set to reduce (such as, a directory containing D source code which exhibits a particular compiler bug)
  • an oracle (or, more mundanely, a test script), which itself:
  • takes as input a variation of the data set, and
  • produces a yes-or-no answer on whether the input still satisfies the sought property (such as reproducing the particular compiler bug).

DustMite’s output is some local minimum variation of the data set, which it reaches by consecutively trying to remove parts of the data set and saving the results which the oracle approves. In the compiler bug example, this means removing bits of code which are not required to reproduce the problem at hand.

DustMite wouldn’t be very efficient if it attempted to remove things line-by-line or character-by-character. In order to maximize the chance of finding good reductions, the input is parsed into a tree according to the syntax of the input files.

Each tree node consists of a “head” (string), children (list of node pointers), and “tail” (string). Technically, it is redundant to have both “head” and “tail”, but they make representing some constructs and performing reductions much simpler, such as paren/bracket pairs.

Nodes are arranged into a binary tree as an optimization.

Additionally, nodes may have a list of dependencies. The dependency relationship essentially means “if this node is removed, these nodes should be removed too”. These constraints are not representable using just the tree structure described above, and are used to allow reducing things such as lists where trailing item delimiters are not allowed, or removing a function parameter and corresponding arguments from the entire code base at once.

In the case of D source code, declarations, statements, and subexpressions get their own tree nodes, so that they can be removed in one go if unneeded. The parser DustMite uses for D source code is intentionally very simple because it needs to handle potentially invalid D code, and you don’t want your bug reduction tool to also crash on top of the compiler.

How DustMite sees a simple D program.

An algorithm decides the order in which nodes are queued for potential deletion; DustMite implements several (it calls them “strategies”). Fundamentally, a strategy’s interface is (statei, resulti) ⇒ (statei+1, reductioni+1), i.e., which reduction is chosen next depends on the previous reduction and its result. The default “inbreadth” strategy visits nodes in ascending depth order (row by row) and starts over from the top as long as it finds new reductions.

DustMite today supports quite a few more options:

The current version.

Probably, the most interesting of these is the -j switch—one reason being that DustMite’s task is inherently not parallelizable. Which reduction is chosen next, and the tree version to which that reduction is applied, depends on the previous reduction’s result.

DustMite works around this by putting unused CPU cores to work on lookahead: using a highly sophisticated predictor, it guesses what the result of the current reduction will be, and based on that assumption, calculates the next reduction. If the guess was right, great! We get to use that result. Otherwise, the work is wasted. Implementing this meant that strategies now needed to have copyable state, and so had to be refactored from an imperative style to a state machine.

Unfortunately, although the highly expected feature was implemented four years ago, the initial implementation was rather underwhelming. DustMite still did too much work in the main thread and wasted too much CPU time on rescanning the data set tree on every reduction. The problem was so bad that, at high core counts, lookahead mode was even slower than single-threaded mode.

I have recently set out to resolve these inadequacies. The following obstacles stood in the way:

Problem 1: Hashing was too slow. Because the oracle’s services (i.e., running the test script) are usually expensive, DustMite keeps track of a cache of previously attempted reductions and their outcome. This helps because not all tree transformations result in a change of output, and some strategies will retry reductions in successive iterations. A hash of the tree is used as the cache key; however, calculating it requires walking the entire tree every time, which is slow for large inputs.

Would it be possible to make the hash calculation incremental? One approach would be Merkle trees (each node’s hash is the hash of its children’s hashes), however that is suboptimal in the case of e.g., empty leaf nodes. CS erudite Ivan Kazmenko blesses us with an answer: polynomial hashes! By representing strings as polynomials, it is possible to use modulo arithmetic to calculate an incremental fixed-size hash and cache subtree hashes per node.

Each node holds its cumulative hash and length.

The number theory staggered me at first, so I recruited the assistance of feep from #d. After we went through a few draft implementations, I could begin working on the final version. The first improvement was replacing the naive exponentiation algorithm with exponentiation by squaring (D CTFE allowed precomputing a table at compile-time and a faster calculation than the classical method). Next, there was the matter of the modulo.

Initially, we used integer overflow for modulo arithmetic (i.e. q=264), however Ivan cautioned against using powers of two as the modulo, as this makes the algorithm susceptible to Thue-Morse strings. Not long ago I was experimenting with using long multiplication/division CPU instructions (where multiplying one machine word by another yields the result in two machine words with a high and low part, and vice-versa for division). D allows generating assembler code specific to the types that the function template is instantiated with, though in DustMite we only use the unsigned 64-bit variant (on x86 we fall back to using integer overflow).

With the hashing algorithm implemented, all that remained was to mark dirty nodes (they or their children had their content edited) and incrementally recalculate their hashes as needed. Dependencies posed a small obstacle: at the time, they were implemented as simply an array of pointers to the dependency node within the tree. As such, we didn’t know how to get to their parents (to mark them dirty as well), however this was easily overcome by adding a “parent” pointer to each node.

Well, or so I thought, until I got to work on the next problem.

Problem 2: Copying the tree. At the time, the current version of the tree representing the data set was part of the global state. Because of this, applying a reduction was implemented twice:

This was clumsy, but faster and less complicated than making a copy of the entire tree just to change one part of it to test a reduction. However, doing so was a requirement for proper lookahead, otherwise we would be unable to test reductions based on results where past tests predicted a positive outcome, or do nearly anything in a separate thread.

One issue was the tree “internal pointers”—making a copy would require updating all pointers within the tree to point to the new copies in the new tree. This was easy for children/parent pointers (since we can reliably visit every such pointer exactly once), but not quite for dependencies: because they were also implemented as simple pointers to nodes, we would have to keep track of a map of which node was copied where in order to update the dependency pointers.

One way to solve this would be to change the representation of node references from pointers to indices into a node array; this way, copying the tree would be as simple as a .dup. However, large inputs meant many nodes, and I wanted to see if it was possible to avoid iterating over every node in the tree (i.e. O(n)) for every reduction.

Was it possible? It would mean that we would copy only the modified nodes and their parents, leaving the rest of the tree in-place, and only reusing it as the copies’ children. This goal conflicted with the existence of “parent” pointers, because a parent would have to point towards either the old or new root, so to resolve this ambiguity every node would have to be copied. As a result, the way we handled dependencies needed to be rethought.

Editing trees with “copy on write” involves copying just the edited nodes (🔴), and their parents.

With internal pointers out, the next best thing to array indices for referencing a node was a series of instructions for how to reach the node from the tree root: an address. The representation of these addresses that I chose was a bit string represented as a linked list, where each list node holds the child index at that depth, starting from the deep end. Such a representation can be arranged in a tree where the root-side ends are shared, mimicking the structure of the tree containing the nodes for the reduced data, and thus allowing us to reuse memory and minimize allocations.

Nodes cannot hold their own address (as that would make them unmovable),
which is why they need to be stored outside of the main tree.

For addresses to work, the object they point at needs to remain the same, which means that we can no longer simply remove children from tree nodes—an address going through the second child would become invalid if the first child was removed. Rewriting all affected addresses for every tree edit is, of course, impractical, which leads us to the introduction of tombstones—dead nodes that only serve to preserve the index of the children that follow it. Because one of the possible reduction types involves moving subtrees around the tree, we now also have “redirects” (which are just tombstones with a “see here” address attached).

With the above changes in place, we can finally move forward with fixing and optimizing lookahead, as well as implementing incremental rehashing in a way that’s compatible with the above! The mutable global “current” tree variable is gone, save now simply takes a tree root as an argument, and applyReduction is now:

/// Apply a reduction to this tree, and return the resulting tree.
/// The original tree remains unchanged.
/// Copies only modified parts of the tree, and whatever references them.
Entity applyReduction(Entity origRoot, ref Reduction r)

With the biggest hurdle behind us, and a few more rounds of applying Walter Bright’s secret weapon, the performance metrics started to look more like what they should:

Going deeper would likely involve using OS-specific I/O APIs or rewriting D’s GC.

A mere 3.5x speed-up from a 32-fold increase in computational power may seem underwhelming. Here are some reasons for this:

  • With a 50/50 predictor, predictions form a complete binary tree, so doubling the number of parallel jobs gives you +1x more speed. That’s roughly log₂(jobs)-1, or 4 for 32 jobs – not far off!

  • The results will depend on the reduction being performed, so YMMV. For a certain artificial test case, one optimization (not pictured above) yielded a 500x speed-up!

  • DustMite does not try to keep all CPU cores busy all the time. If a prediction turns out false, all lookahead jobs based on it become wasted work, so DustMite only starts new lookahead tasks when a reduction’s outcome is resolved. Perhaps ideally DustMite would keep starting new jobs but kill them as soon as it discovers they’re based on a misprediction. As there is no cross-platform process group management in Phobos, the D standard library, this is something I left for future versions.

  • Some work is still done in the main thread, because moving it to a worker thread actually makes things slower due to the global GC lock.

There still remains one last place where DustMite iterates over every tree node per reduction: saving the tree to disk (so that it could be read by the test script). This seems unavoidable at first, but could actually be avoided by caching each node’s full text contents within the node itself.

I opted to leave this one out. With the other related improvements, such as using lockingBinaryWriter and aggregating writes of contiguous strings as one I/O operation, the increase in memory usage was much more dramatic than the decrease in execution time, even when optimized to just one allocation per reduction (polynomial hashing gives us every node’s total length for free). But, for a brief instant, DustMite processed reductions in sub-O(n) time.

One more addition is worth mentioning: Andrej Mitrovic suggested a switch which would replace removed text with whitespace, which would allow searching for exact line numbers in the test script. At the time, its addition posed significant challenges, as there needed to be some way to keep removed nodes in the tree but exclude them from future removal attempts. With the new tree representation, this became much easier, and also allowed creating the following animation:

In conclusion, I’d like to bring up that DustMite is good at more than just reducing compiler test cases. The wiki lists some ideas:

  • Finding the source of ambiguous or misleading compiler error messages (e.g., errors with the file/line information pointing only inside the standard library).

  • Alternative (much slower, but also much more thorough) method of verifying unit test code coverage. Just because a line of code is executed, that doesn’t mean it’s necessary; DustMite can be made to remove all code that does not affect the execution of your unit tests.

  • Similarly, if you have complete test coverage, it can be used for reducing the source tree to a minimal tree which includes support for only enabled unittests. This can be used to create a version of a program or library with a test-defined subset of features.

  • The --obfuscate mode can obfuscate your code’s identifiers. It can be used for preparing submission of proprietary code to bug trackers.

  • The --fuzz mode (a new feature) can help find bugs in compilers and tools by creating random programs (using fragments of other programs as input).

But DustMite is not limited to D programs (or any kind of programs) as input. With the --split option, we can tell DustMite how to parse and reduce other kinds of files. DustMite successfully handled the following scenarios:

  • reducing C++ programs (the D parser supports some C++-only syntax too);

  • reducing Python programs (using the indent split mode);

  • reducing a large commit to a minimal diff (using the diff split mode);

  • reducing a commit list, when git bisect is insufficient due to the problem being introduced across more than any single commit;

  • reducing a large data set to a minimal one, resulting in the same code coverage, with the purpose of creating a test suite;

  • and many more which I do not remember.

Today, some version of DustMite is readily available in major distributions (usually as part of some D-related package), so I’m happy having a favorite tool one apt-get / pacman -S away when I’m not at my PC.

Discovering a problem which can be elegantly reduced away by DustMite is always exciting for me, and I’m hoping you will find it useful too.

D 2.091.0 Released

Digital Mars D logoThe latest release of DMD, the D reference compiler, ships with 18 major changes and 66 bugfixes from 55 contributors. This release contains, among other goodies, improvements to the Windows experience and enhancements to C and C++ interoperability. As fate would have it, the initial release announcement came in the aftermath of some unfortunate news regarding DConf 2020.

DMD on Windows

Over the years, some D users have remarked that the development of D is Linux-centric, that Windows is the black sheep or red-headed stepchild of D platforms. For anyone familiar with D’s early history, that seems an odd thing to say, given that DMD started out as a Windows-only compiler that could only output 32-bit objects in the OMF format. But it’s also understandable, as anyone not familiar with that history could only see that DMD on Windows lagged behind the Linux releases.


One place where the official DMD releases on Windows have continued to differ from the releases on other platforms is the lack of 64-bit binaries in the release packages. Again, there’s a historical reason for this. The default output of the compiler is determined by how it is compiled, e.g., 32-bit versions output 32-bit binaries by default. When Walter first added support to DMD for 64-bit output on Windows, it required giving the back end the ability to generate object files in Microsoft’s version of the COFF format and also requiring users to install the Microsoft Build Tools and Platform SDK for access to the MS linker and system link libraries. This is quite a different experience from other platforms, where you can generally expect a common set of build tools to have been installed via the system package manager on any system set up for C and C++ development.

For a Windows developer who chooses GCC for their C and C++ development (or who does no C or C++ development at all), it’s a big ask to require them to download and install several GBs they might not already have installed and probably will never use for anything else. So D releases on Windows continued to ship with 32-bit binaries and the OPTLINK linker in order to provide a minimum out-of-the-box experience. That was a perfectly fine solution, unless you happened to be someone who really wanted 64-bit output (posts from disgruntled Windows users who didn’t want to install the MS tools can be found sprinkled throughout the forum archives).

Eventually, the LLVM linker (LLD) was added to the DMD Windows release packages, along with system link libraries generated from the MinGW definitions. This allowed users to compile 64-bit output out of the box and, once the kinks were worked out, eliminated the dependency on the MS linker. Yet, the official release packages still did not include a 64-bit version of DMD and still did not support 64-bit output by default.

With DMD 2.091.0, the black sheep has come back into the fold. The official DMD releases on Windows now ship with 64-bit binaries, so those of you masochists out there who cling to Makefiles and custom build scripts can expect the default output be what you expect it to be (for the record, DUB, the build tool and package manager that ships with DMD, has been instructing the compiler to compile 64-bit output by default on 64-bit systems for the past few releases).

Windows gets even more love

There are lots of goodies for Windows in this release. Another biggie is that DMD is now 30-40% faster on Windows. It’s no secret that LDC, the LLVM-based D compiler, generates faster binaries than DMD (for some D users, the general rule of thumb is to develop with DMD for its fast compile times and release with LDC for its faster binaries, though others argue that LDC is plenty fast for development and DMD is fine for production). There have been requests for some time to stop compiling DMD with DMD and start doing it with LDC instead. This release is the first to put that into practice.

There are a number of smaller enhancements to the Windows experience: the script available on the DMD downloads page that some people prefer now supports POSIX environments on Windows; the system link libraries that ship with the compiler have been upgraded from MinGW  5.0.2 to 7.0.0; LLD has been upgraded to 9.0.0; and there’s plenty more in the changelog.

C++ Header Generation

With just about every major release of DMD, D’s interoperability with C and C++ sees some kind of improvement. This release brings a huge one.

Over the years, some have speculated that it would be excellent if the D compiler could generate headers for C and C++ for D libraries intended to be usable in C or C++ programs. Now that wishful thinking has become a(n experimental) reality. Given a set of extern(C) or extern(C++) functions, DMD can generate header files that contain the appropriate C or C++ declarations. Three compiler switches get the job done:

  • -HC will cause the header to be generated and printed to standard output
  • -HCf=fileName will cause the header to be generated and printed to the specified file
  • -HCd=directoryname will (once it’s implemented) cause the header to be printed to a file in the specified directory

See the changelog for example output.

Other News

While the Corona virus was initially ramping up out of sight from most of the world, plans for DConf 2020 were ramping up online from different locations around the world. Planning began in November, the venue was secured in late December, and the website launched with the announcement in early January.

As news of the virus outbreak spread, the conference organizers grew concerned. Would we be okay in June? In late February, that concern manifested as a discussion of possible contingency plans. Two weeks later, it resulted in the decision to cancel DConf 2020. Thankfully, the D community has been supportive of the decision.

As part of the discussion of contingency plans, the possibility was raised of hosting an online conference. The idea of course came up in the discussion of the cancellation in the forums, and a few people reached out shortly after the initial announcement offering to provide help in setting something up. Walter created a forum thread to discuss the topic for anyone interested.

No one involved with organizing DConf has any experience with hosting an online conference. We’re currently exploring options and looking at what the organizers of other Conferences in the Time of COVID-19 are doing. We want to do it, and we want to do it well. Experience with organizing DConf in the real world has taught us not to jump on any old technology without first having a fallback (ahem, DConf 2018 livestream) and making sure the tech does what we expect it to (ahem, DConf 2019 livestream). So don’t expect a quick announcement. We want to find the right tech that fits our requirements and explore how it works before we move forward with setting dates. But do expect that DConf 2020 Online is looking more and more likely to become a thing.

Tracing D Applications

At one time or another during application development you need to make a decision: does your application work like it should and, if not, what is wrong with it? There are different techniques to help you decide, some of which are logging, tracing, and profiling. How are they different? One way to look at it is like this:

  • when you know exactly the events you are interested in to make the decision, you use logging
  • when you don’t know exactly the events you need to make a decision and you are forced to collect as many events as you can, you use tracing
  • when you need to collect some events and analyze them to derive new information, you use profiling

In this article, we focus on tracing.

When developing an application, you can use tracing to monitor its characteristics at run time to, for example, estimate its performance or memory consumption. There are several options to do so, and some of them are:

  • means provided by the programming language (for example, using D’s writef, a.k.a. printf debugging)
  • debuggers (using scripts or remote tracing)
  • OS-specific tracing frameworks (linux {k|u}probes and usdt probes, linux kernel event, performance events in windows etc)

In this article, the following contrived D example is used to help illustrate all three cases. We’ll be focusing on Linux. All example code in this article can be found in the GitHub repository at

foreach(counter; 0..total_cycles)
    // randomly generate one of three kinds of event
    Event event = cast(Event) uniform(0, 3);

    // "perform" the job and benchmark its CPU execution time
    switch (event)
        case Event.One:


        case Event.Two:


        case Event.Three:



doSomeWork simulates a CPU-intensive job by using DRuntime’s Thread.sleep method. This is a very common pattern where an application runs in cycles and, on every iteration, performs a job depending on the application state. Here we can see that the application has three code paths (CaseOne, CaseTwo, and CaseThree). We need to trace the application at run time and collect information about its timings.

The writef-Based Approach

Using writef/ln from Phobos, D’s standard library, to trace the application is naive, but can be very useful nevertheless. The code from tracing_writef.d:

    case Event.One:
            auto sw = StopWatch(;


            writefln("%d:\tEvent %s took: %s", counter, event, sw.peek);

With this trivial approach, StopWatch from the standard library is used to measure the execution time of the code block of interest. Compile and run the application with the command dub tracing_writef.d and look at its output:

Running ./example-writef
0:      Event One took:   584 ms, 53 μs, and 5 hnsecs
1:      Event One took:   922 ms, 72 μs, and 6 hnsecs
2:      Event Two took:   1 sec, 191 ms, 73 μs, and 8 hnsecs
3:      Event Two took:   974 ms, 73 μs, and 7 hnsecs

There is a price for this—we need to compile tracing code into our binary, we need to manually implement the collection of tracing output, disable it when we need to, and so on—and this means the size of the binary increases. To summarize:


  • all the might of Phobos is available to employ (except when in BetterC mode)
  • tracing output can be displayed in a human readable format (look at the nice output of Duration above; thanks to Jonathan M. Davis for the std.datetime package)
  • source code is portable
  • easy to use
  • no third-party tools required


  • the application must be rebuilt and restarted in order to make any changes, which is inappropriate for some applications (such as servers)
  • no low-level access to the application state
  • noise in the code due to the addition of tracing code
  • can be unusable due to a lot of debug output
  • overhead can be large
  • can be hard to use in production

This approach is very suitable in the early stages of development and less useful in the final product. Although, if the tracing logic is fixed and well defined, this approach can be used in production-ready applications/libraries. For example, this approach was suggest by Stefan Koch for tracing the DMD frontend to profile performance and memory consumption.

Debugger-Based Approach

The debugger, in this case GDB, is a more advanced means to trace applications. There is no need to modify the application to change the tracing methodology, making it very useful in production. Instead of compiling tracing logic into the application, breakpoints are set. When the debugger stops execution on a breakpoint, the developer can use the large arsenal of GDB functionality to inspect the internal state of the inferior (which, in GDB terms, usually refers to the process being debugged). It is not possible in this case to use Phobos directly, but helpers are available and, moreover, you have access to registers and the stack—options which are unavailable in the case of writef debugging.

Let’s take a look the code from tracing_gdb.d for the first event:

    case Case.One:



As you can see, now there is no tracing code and it is much cleaner. The tracing logic is placed in a separate file called trace.gdb. It consists of a series of command blocks configured to execute on specific breakpoints, like this:

set pagination off
set print address off

break app.d:53
set $EventOne = currClock()

break app.d:54
set $EventOne = currClock() - $EventOne
printf "%d:\tEvent One   took: %s\n", counter, printClock($EventOne)



In the first line, pagination is switched off. This enables scrolling so that there is no need to press “Enter” or “Q” to continue script execution when the current console fills up. The second line disables showing the address of the current breakpoint in order to make the output less verbose. Then breakpoints are set on lines 53 and 54, each followed by a list of commands (between the commands and end labels) that will be executed when GDB stops on these breakpoints. The breakpoint on line 53 is configured to fetch the current timestamp (using a helper) before doSomeWork is called, and the one on line 54 to get the current timestamp after doSomeWork has been executed. In fact, line 54 is an empty line in the source code, but GDB is smart enough to set the breakpoint on the next available line. $EventOne is a convenience variable where we store the timestamps to calculate code execution time. currClock() and printClock(long) are helpers to let us prettify the formatting by means of Phobos. The last two commands in the script initiate the debugging and quit the debugger when it’s finished.

To build and run this tracing session, use the following commands:

dub build tracing_gdb.d --single
gdb --command=trace.gdb ./tracing-gdb | grep Event

trace.gdb is the name of the GDB script and tracing-gdb is the name of the binary. We use grep to make the GDB output look like writefln output for easier comparison.


  • the code is clean and contains no tracing code
  • there is no need to recompile the application to change the tracing methodology—in many cases, it’s enough to simply change the GDB script
  • there is no need to restart the application
  • it can be used in production (sort of)
  • there is no overhead if/when not tracing and little when tracing
  • watchpoints and catchpoints can be used instead of breakpoints


  • using breakpoints in some cases may be inconvenient, annoying, or impossible.
  • GDB’s pretty-printing provides “less pretty” output because of the lack of full Phobos support compared to the writef approach
  • sometimes GDB is not available in production

The point about setting breakpoints in GDB being inconvenient is based on the fact that you can use only an address, a line number, or a function name (see the gdb manual). Using an address is too low level and inconvenient. Line numbers are ephemeral and can easily change when the source file is edited, so the scripts will be broken (this is annoying). Using function names is convenient and stable, but is useless if you need to place a tracing probe inside a function.

A good example of using GDB for tracing is Vladimir Panteleev’s dmdprof.

The USDT-Based Approach

So far we have two ways to trace our application that are complimentary, but is there a way to unify all the advantages of these two approaches and avoid their drawbacks? Of course, the answer is yes. In fact there are several ways to achieve this, but hereafter only one will be discussed: USDT (Userland Statically Defined Tracing).

Unfortunately, due to historical reasons, the Linux tracing ecosystem is fragmented and rather confusing. There is no plain and simple introduction. Get ready to invest much more time if you want to understand this domain. The first well-known, full-fledged tracing framework was DTrace, developed by Sun Microsystems (now it is open source and licensed under the GPL). Yes, strace and ltrace have been around for a long time, but they are limited, e.g., they do not let you trace what happens inside a function call. Today, DTrace is available on Solaris, FreeBSD, macOS, and Oracle Linux. DTrace is not available in other Linux distributions because it was initially licensed under the CDDL. In 2018, it was relicensed under the GPL, but by then Linux had its own tracing ecosystem. As with everything, Open Source has disadvantages. In this case, it resulted in fragmentation. There are now several tools/frameworks/etc. that are able to solve the same problems in different ways but somehow and sometimes can interoperate with each other.

We will be using bpftrace, a high level tracing language for Linux eBPF. In D, USDT probes are provided by the usdt library. Let’s start from the code in tracing_usdt.d:

	case Case.One:
		mixin(USDT_PROBE!("dlang", "CaseOne", kind));


		mixin(USDT_PROBE!("dlang", "CaseOne_return", kind));

Here we mixed in two probes at the start and the end of the code of interest. It looks similar to the first example using writef, but a huge difference is that there is no logic here. We only defined two probes that are NOP instructions. That means that these probes have almost zero overhead and we can use them in production. The second great advantage is that we can change the logic while the application is running. That is just impossible when using the writef approach. Since we are using bpftrace, we need to write a script, called, to define actions that should be performed on the probes:

	@last["CaseOne"] = nsecs;

	if (@last["CaseOne"] != 0)
		$tmp = nsecs;
		$period = ($tmp - @last["CaseOne"]) / 1000000;
		printf("%d:\tEvent CaseOne   took: %d ms\n", @counter++, $period);
		@last["CaseOne"] = $tmp;
		@timing = hist($period);

The first statement is the action block. It triggers on the USDT probe that is compiled in the ./tracing-usdt executable (it includes the path to the executable) with the dlang provider name and the CaseOne probe name. When this probe is hit, then the global (indicated by the @ sign) associative array last updates the current timestamp for its element CaseOne. This stores the time of the moment the code starts running. The second action block defines actions performed when the CaseOne_return probe is hit. It first checks if corresponding element in the @last associative array is already initialized. This is needed because the application may already be running when the script is executed, in which case the CaseOne_return probe can be fired before CaseOne. Then we calculate how much time code execution took, output it, and store it in a histogram called timing.

The BEGIN and END blocks at the top of define actions that should be performed at the beginning and the end of script execution. This is nothing more than initialization and finalization. Build and run the example with:

dub build tracing_usdt.d   --single --compiler=ldmd2 # or gdc
./tracing-usdt &                                     # run the example in background
sudo bpftrace                            # start tracing session


Attaching 8 probes...
0:	Event CaseThree took: 552 ms
1:	Event CaseThree took: 779 ms
2:	Event CaseTwo   took: 958 ms
3:	Event CaseOne   took: 1174 ms
4:	Event CaseOne   took: 1059 ms
5:	Event CaseThree took: 481 ms
6:	Event CaseTwo   took: 1044 ms
7:	Event CaseThree took: 611 ms
8:	Event CaseOne   took: 545 ms
9:	Event CaseTwo   took: 1038 ms
10:	Event CaseOne   took: 913 ms
11:	Event CaseThree took: 989 ms
12:	Event CaseOne   took: 1149 ms
13:	Event CaseThree took: 541 ms
14:	Event CaseTwo   took: 1072 ms
15:	Event CaseOne   took: 633 ms
16:	Event CaseTwo   took: 832 ms
17:	Event CaseTwo   took: 1120 ms

[256, 512)             1 |@@@@@                                               |
[512, 1K)             10 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@|
[1K, 2K)               7 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@                |

In the session output above there are only 18 lines instead of 20; it’s because tracing-usdt was started before the bpftrace script so the first two events were lost. Also, it’s necessary to kill the example by typing Ctrl-C after tracing-usdt completes. After the bpftrace script stops execution, it ouputs the contents of the timing map as a histogram. The histogram says that one-time code execution takes between 256 and 512 ms, ten times between 512 and 1024 ms, and seven times more between 1024 and 2048 ms. These builtin statistics make using bpftrace easy.


  • provides low-level access to the code (registers, memory, etc.)
  • minimal noise in the code
  • no need to recompile or restart when changing the tracing logic
  • almost zero overhead
  • can be effectively used in production


  • learning USDT can be hard, particularly considering the state of the Linux tracing ecosystem
  • requires external tools (frontends)
  • OS specific

Note: GDB has had support for USDT probes since version 7.5. To use it, modify the trace.gdb script to set breakpoints using USDT probes instead of line numbers. That eases development because it eliminates the need to synchronize line numbers during source code modification.

Futher reading:


Feature writef gdb usdt
by means of Phobos
and other libs
by means of
limited builtins
low-level no yes yes
clean code no yes sort of
recompilation yes no no
restart yes no no
easy easy+ advanced
no only debugger tracing system front end
cross platform yes sorta of OS specific
overhead can be large none can be ignored
even in production
production ready sometimes possible sometimes impossible yes

Feature descriptions:

  • pretty printing is important if the tracing output should be read by humans (and can be ignored in the case of inter-machine data exchange)
  • low-level means access to low-level details of the traced binary, e.g., registers or memory
  • clean code characterizes whether additional tracing code which is unrelated to the applications’s business logic would be required.
  • recompilation determines if it is necessary to recompile when changing the tracing methodology
  • restart determines if it is necessary to restart the application when changing the tracing methodology
  • usage complexity indicates the level of development experience that may be required to utilize this technology
  • third-party tools describes tools not provided by standard D language distributions are required to use this technology
  • cross platform indicates if this technology can be used on different platforms without changes
  • overhead – the cost of using this technology
  • production ready – indicates if this technology may be used in a production system without consequences

News Update: Swag, Platforms, Documentation Help and More

Here are a few updates on things that have been going on both in front of and behind the scenes of the D Programming Language community.

New D Swag

We’ve got some new items in the DLang Swag Emporium: t-shirts, coffee mugs, and stickers sporting the Royal D logo. (If all Royal D items aren’t showing up for you in the Royal D category, check the D Rocket category. Everything should be in the correct location in a day or two).

You may notice that there are fewer options on the product page than for the other items, i.e. only one mug and sticker, and no dark tee option. They are available, though! When you select one of the existing products, you can change the style of the selection to one of several options. Beware! This may also change the price.

Remember, a small percentage of every item you order from the DLang Swag Emporium goes into the D Language Foundation’s General Fund. Plus, if you click through the link above or on the blog’s sidebar, we’ll get an additional referral fee on top of the item royalty. It’s an easy way to both get some D swag and contribute a few bucks to the Foundation.

Expanded Platform Progress

You maybe aware that some work has been ongoing in getting D onto more platforms. Adam Ruppe was working on contract to get LDC’s Android support to the finish line. He wrapped things up a few weeks back and has been paid out of the Foundation’s HR Fund.

Sebastiaan Koppe has been working on contract to get DRuntime ported to WebAssembly. Progress is ongoing and we currently expect it to be mostly wrapped up by the end of March. Like Adam, he’ll be out of the HR Fund when the contract is complete.

Work is also underway to bring LDC to iOS and iPadOS. We had been hoping to get someone to work on contract for this, but there are few people we know who are familiar enough with the platform to get it done and we were unable to find anyone then with the time to work on it. So we put up a bounty for it and kept our fingers crossed.

Recently, you may have seen forum posts from Jacob Carlborg indicating he’s been working on it in his spare time. Some preliminary support was merged in the LDC 1.20.0 release. Although he isn’t working under contract, he is working toward the bounty. That means anyone who wants to support him can contribute by increasing the bounty. Two contributors have already done so. The base amount of $3000 will be taken from the HR Fund when the work is complete.

And speaking of bounties, there are several others waiting for someone to claim them!

The HR Fund

With one payout from the fund and two coming up, we need to replenish it so we can always have cash earmarked for more contract work and bounties. You can make one-time or recurring donations of any amount directly and receive the same rewards available on our Open Collective page, or you can use a different link to make a $60 donation and get a DConf 2019 t-shirt in return. We’ve still got a few shirts available, so help us get rid of them and boost the HR Fund at the same time!

Documentation Event

Behind-the-scenes discussions about ideas to improve the D ecosystem in one way or another are frequently cycling through the inboxes of the people who can make them happen. Most never see the light of day, but there is one that has great potential. If it all comes together, I’ll be able to announce it in the coming weeks. We need your help to make that happen.

We need some specifics regarding areas where the documentation for D and items in the the D ecosystem is lacking. For example, people often complain about inconsistencies in the D spec, and missing info or examples in the DUB and vibe.d docs.

I’ve started a thread in the D forums where you can post your gripes about incomplete/missing/lackluster documentation. Remember, we need you to be specific. Just saying “the DUB docs are incomplete” doesn’t help. What specifically is missing? Or what specifically is wrong? The more information you can provide the better. And the more examples we can collect the better. The goal is to be able to define specific documentation tasks that anyone with the requisite knowledge can complete.

If we can get enough examples with enough detail, then I should be able to announce a new event sponsored by one of our generous benefactors. And I really want to be able to announce it!

DConf 2020

We really want to see a flood of talk submissions this year. If you’ve never been to DConf, or never presented at any conference, don’t let that stop you! Send us your submission and you may end up with a free trip to the conference.

Also, if you pay for an early-bird registration now (a 15% discount over the regular registration rate) and your talk is selected later, we’ll reimburse your registration fee. So if you’re planning to attend the conference even if your talk isn’t selected, it’s a good idea to register now and avoid the risk of missing the early-bird deadline.

We’re also offering once again the Open Source and Academic Discount; if you are a major open source contributor, a student, or an academic, we’ll give you a 50% discount on the regular registration rate. If you think you qualify, please don’t hesitate to take advantage of it by contacting (or you can contact me directly at for details on how to take advantage.

Finally, we never want to leave anyone out of DConf because they can’t afford to pay. This has been a policy of Walter’s from the beginning. If you are in or around London June 17 – 20 and would like to attend DConf but are unable to afford the registration and/or don’t qualify for the special discount, please email one of the addresses above and we’ll work something out.

DConf 2020: Submission Deadline, Early-Bird Registration, and Invited Keynote

In early January, I announced that Symmetry Investments is bringing DConf back to London for our 2020 edition. At the same time, I said we’d start taking submissions from anyone who wanted to send them in. In the interim, we’ve fixed our deadlines and prepared to start accepting reservations. There was only one thing remaining before I was ready for the formal call for submissions and opening of early-bird registrations: confirming our invited keynote speaker. Now that he has confirmed, it’s all official!

Invited Keynote

We’re excited to welcome Roberto Ierusalimschy to DConf 2020! You may know him from his work as the leading architect of the Lua programming language. He’s the author of Programming in Lua and an Associate Professor of Computer Science at PUC-Rio (the Pontifical Catholic University of Rio de Janeiro).

We don’t know yet what his talk will about, but it can be about any topic he wants. We’ll have more information on that for you when we publish the schedule of all selected talks after April 19.

Call for Submissions

We are accepting submissions for DConf 2020 until April 12. Authors will be notified of their final status by April 19.

We’re eager to see some new faces on the stage this year. If you’ve never presented at a DConf before, please don’t hesitate to send us one or more submissions. One person has already sent in seven!

Unless you’re Roberto Ierusalimschy, we prefer topics that are directly or indirectly related to D. We aren’t intransigent, though, so we’re willing to consider other topics. If someone sends us a proposal that isn’t about D but piques our collective interest, we’ll certainly give it serious consideration.

Having a talk selected is a great way to get to DConf if you’re on a budget. You’ll pay no registration fee, plus we’ll reimburse your transportation and lodging costs (within reason—five-star hotels and business- or first-class plane tickets aren’t on the menu). That’s a pretty good deal.

You can find instructions for writing and submitting your submissions on the DConf 2020 homepage.

Early-Bird Registration

Early-bird registration is available at $340, which is 15% off the regular $400 rate. Because we’re being sponsored by Symmetry in London once more, we once again must include a 20% VAT. So the total early-bird rate is $408 (similarly, the regular rate with VAT will be $480). We’re required by UK law to show you the basic rate and VAT in GBP based on the current HMRC exchange rate. That changes every month, so you can see the latest GPB rates in the registration section of the DConf 2020 homepage.

There, you’ll find options for Flipcause and PayPal. From our perspective, we prefer you use our Flipcause form. That gives you the option to cover the credit card processing fee for us so that 100% of your payment can be put toward DConf expenses. If you choose to uncheck that option, that’s fine, too! It will still save us from paying other fees. Every penny we can put toward the expenses helps.

If you do choose to go through PayPal, you have an option for USD and one for GBP. Some registrants told me last year that they get a GBP option even when clicking the USD button. And of course, some register with GBP-based credit cards. However, the GBP button on the DConf 2020 homepage is a fixed amount based on the current HMRC exchange rate. It changes, but only once a month. It may turn out to be cheaper for you than the rate you get from PayPal or your credit card provider. Of course, it could turn out to be more expensive, so if you’re looking to save a few pounds, you may want to investigate the different exchange rates if they apply to your situation.

And Now For Something Completely Different

DConf isn’t the only event Symmetry Investments is sponsoring these days. We recently wrapped up the 2019 edition of the Symmetry Autumn of Code.

This year, we started with five participants working on five interesting projects. Each participant was to complete a total of four milestones over four months with guidance from a mentor. At the successful completion of the first three milestones, each participant would receive $1000. At the end of the fourth and final milestone, one participant would be selected to receive one more $1000 payment and an all-expense paid trip to DConf.

As the event played out, we lost one of the participants at the end of Milestone 2. Two more were unable to fully commit to the Milestone 4 deadline (though they promised to continue working on their projects after SAOC). That left two participants for the SAOC review committee to select from. It was a very difficult decision, as both participants did excellent work and received glowing evaluations from their mentors.

Now I can announce that the SAOC 2019 finalist was Roberto Rosmaninho!

Roberto, with his mentor Nicholas Wilson, worked on adding support for Multi-Level Intermediate Representation (MLIR) to LDC, the LLVM-based D compiler. He is currently working on putting together pull requests for LDC and intends to work on optimizations going forward. He has also confirmed that he will take advantage of his reward so that we will have at least two Robertos at DConf this year.

As we did last year with Francesco Gallà, the SAOC 2018 finalist, we’ve asked Roberto to submit a talk this year. He promised to do so. We can’t promise his talk will be selected (though the odds are high out of the gate), but he still gets a free trip if it isn’t! Besides, we’re looking forward to meeting him.

On behalf of the D Language Foundation and Symmetry Investments, I want to thank everyone who participated in SAOC 2019. Keep an eye on this blog for news about future events.

Now go prep your DConf 2020 submissions!

wc in D: 712 Characters Without a Single Branch

After reading “Beating C With 80 Lines Of Haskell: Wc”, which I found on Hacker News, I thought D could do better. So I wrote a wc in D.

The Program

It consists of one file and has 34 lines and 712 characters.

import std.stdio : writefln, File;
import std.algorithm : map, fold, splitter;
import std.range : walkLength;
import std.typecons : Yes;
import std.uni : byCodePoint;

struct Line {
	size_t chars;
	size_t words;

struct Output {
	size_t lines;
	size_t words;
	size_t chars;

Output combine(Output a, Line b) pure nothrow {
	return Output(a.lines + 1, a.words + b.words, a.chars + b.chars);

Line toLine(char[] l) pure {
	return Line(l.byCodePoint.walkLength, l.splitter.walkLength);

void main(string[] args) {
	auto f = File(args[1]);
	Output o = f
		.map!(l => toLine(l))
		.fold!(combine)(Output(0, 0, 0));

	writefln!"%u %u %u %s"(o.lines, o.words, o.chars, args[1]);

Sure, it is using Phobos, D’s standard library, but then why wouldn’t it? Phobos is awesome and ships with every D compiler. The program itself does not contain a single if statement. The Haskell wc implementation has several if statements. The D program, apart from the main function, contains three tiny functions. I could have easily put all the functionally in one range chain, but then it probably would have exceeded 80 characters per line. That’s a major code-smell.

The Performance

Is the D wc faster than the coreutils wc? No, but it took me 15 minutes to write mine (I had to search for walkLength, because I forgot its name).

file lines bytes coreutils haskell D
app.d 46 906 3.5 ms +- 1.9 ms 39.6 ms +- 7.8 ms 8.9 ms +- 2.1 ms
big.txt 862 64k 4.7 ms +- 2.0 ms 39.6 ms +- 7.8 ms 9.8 ms +- 2.1 ms
vbig.txt 1.7M 96M 658.6ms +- 24.5ms 226.4 ms +- 29.5 ms 1.102 s +- 0.022 s
vbig2.txt 12.1M 671M 4.4 s +- 0.058 s 1.1 s +- 0.039 s 7.4 s +- 0.085 s


file coreutils haskell D
app.d 2052K 7228K 7708K
big.txt 2112K 7512K 7616K
vbig.txt 2288K 42620K 7712K
vbig2.txt 2360K 50860K 7736K

Is the Haskell wc faster? For big files, absolutely, but then it is using threads. For small files, GNU’s coreutils still beats the competition. At this stage my version is very likely IO bound, and it’s fast enough anyway.

I’ll not claim that one language is faster than another. If you spend a chunk of time on optimizing a micro-benchmark, you are likely going to beat the competition. That’s not real life. But I will claim that functional programming in D gives functional programming in Haskell a run for its money.

A Bit About Ranges

Digital Mars D logoA range is an abstraction that you can consume through iteration without consuming the underlying collection (if there is one). Technically, a range can be a struct or a class that adheres to one of a handful of Range interfaces. The most basic form, the InputRange, requires the function

void popFront();

and two members or properties:

T front;
bool empty;

T is the generic type of the elements the range iterates.

In D, ranges are special in a way that other objects are not. When a range is given to a foreach statement, the compiler does a little rewrite.

foreach (e; range) { ... }

is rewritten to

for (auto __r = range; !__r.empty; __r.popFront()) {
    auto e = __r.front;

auto e = infers the type and is equivalent to T e =.

Given this knowledge, building a range that can be used by foreach is easy.

struct Iota {
	int front;
	int end;

	@property bool empty() const {
		return this.front == this.end;

	void popFront() {

unittest {
	import std.stdio;
	foreach(it; Iota(0, 10)) {

Iota is a very simple range. It functions as a generator, having no underlying collection. It iterates integers from front to end in steps of one. This snippet introduces a little bit of D syntax.

@property bool empty() const {

The @property attribute allows us to use the function empty the same way as a member variable (calling the function without the parenthesis). The trailing const means that we don’t modify any data of the instance we call empty on. The built-in unit test prints the numbers 0 to 10.

Another small feature is the lack of an explicit constructor. The struct Iota has two member variables of type int. In the foreach statement in the test, we create an Iota instance as if it had a constructor that takes two ints. This is a struct literal. When the D compiler sees this, and the struct has no matching constructor, the ints will be assigned to the struct’s member variables from top to bottom in the order of declaration.

The relation between the three members is really simple. If empty is false, front is guaranteed to return a different element, the next one in the iteration, after a call to popFront. After calling popFront the value of empty might have changed. If it is true, this means there are no more elements to iterate and any further calls to front are not valid. According to the InputRange documentation:

  • front can be legally evaluated if and only if evaluating empty has, or would have, equaled false.
  • front can be evaluated multiple times without calling popFront or otherwise mutating the range object or the underlying data, and it yields the same result for every evaluation.

Now, using foreach statements, or loops in general, is not really functional in my book. Lets say we want to filter all uneven numbers of the Iota range. We could put an if inside the foreach block, but that would only make it worse. It would be nicer if we had a range that takes a range and a predicate that can decide if an element is okay to pass along or not.

struct Filter {
	Iota input;
	bool function(int) predicate;

	this(Iota input, bool function(int) predicate) {
		this.input = input;
		this.predicate = predicate;

	void testAndIterate() {
				&& !this.predicate(this.input.front))

	void popFront() {

	@property int front() {
		return this.input.front;

	@property bool empty() const {
		return this.input.empty;

bool isEven(int a) {
	return a % 2 == 0;

unittest {
	foreach(it; Filter(Iota(0,10), &isEven)) {

Filter is again really simple: it takes one Iota and a function pointer. On construction of Filter, we call testAndIterate, which pops elements from Iota until it is either empty or the predicate returns false. The idea is that the passed predicate decides what to filter out and what to keep. The properties front and empty just forward to Iota. The only thing that actually does any work is popFront. It pops the current element and calls testAndIterate. That’s it. That’s an implementation of filter.

Sure, there is a new while loop in testAndIterate, but rewriting that with recursion is just silly, in my opinion. What makes D great is that you can use the right tool for the job. Functional programming is fine and dandy a lot of the time, but sometimes it’s not. If a bit of inline assembly would be necessary or nicer, use that.

The call to Filter still does not look very nice. Assuming, you are used to reading from left to right, Filter comes before Iota, even though it is executed after Iota. D has no pipe operator, but it does have Uniform Function Call Syntax (UFCS). If an expression can be implicitly converted to the first parameter of a function, the function can be called like it is a member function of the type of the expression. That’s a lot of words, I know. An example helps:

string foo(string a) {
	return a ~ "World";

unittest {
	string a = foo("Hello ");
	string b = "Hello ".foo();
	assert(a == b);

The above example shows two calls to the function foo. As the assert indicates, both calls are equivalent. What does that mean for our Iota Filter example? UFCS allows us to rewrite the unit test to:

unittest {
	foreach(it; Iota(1,10).Filter(&isEven)) {

Implementing a map/transform range should now be possible for every reader. Sure, Filter can be made more abstract through the use of templates, but that’s just work, nothing conceptually new.

Of course, there are different kinds of ranges, like a bidirectional range. Guess what that allows you to do. A small tip: a bidirectional range has two new primitives called back and popBack. There are other range types as well, but after you understand the input range demonstrated twice above, you pretty much know them all.

P.S. Just to be clear, do not implement your own filter, map, or fold; the D standard library Phobos has everything you every need. Have a look at std.algorithm and std.range.

About the Author

Robert Schadek received a master’s degree in Computer Science at the University of Oldenburg. His master thesis was titled “DMCD A Distributed Multithreading Caching D Compiler” where he work on building a D compiler from scratch. He was a computer science PhD student from 2012–2018 at the University of Oldenburg. His PhD research focuses on quorum systems in combination with graphs. Since 2018 he is happily using D in his day job working for Symmetry Investments.

What is Symmetry Investments?

Symmetry Investments is a global investment company with offices in Hong Kong, Singapore and London. We have been in business since 2014 after successfully spinning off from a major New York-based hedge fund.

At Symmetry, we seek to engage in intelligent risk-taking to create value for our clients, partners and employees. We derive our edge from our capacity to generate Win-Wins – in the broadest sense. Win-Win is our fundamental ethical and strategic principle. By generating Win-Wins, we can create unique solutions that reconcile perspectives that are usually seen as incompatible or opposites, and encompass the best that each side has to offer. We integrate fixed-income arbitrage with global macro strategies in a novel way. We invent and develop technology that focuses on the potential of human-machine integration. We build systems where machines do what they do best, supporting people to do what people do best. We are creating a collaborative meritocracy: a culture where individual contribution serves both personal and collective goals – and is rewarded accordingly. We value both ownership thinking AND cooperative team spirit, self-realisation AND community.

People at Symmetry Investments have been active participants in the D community since 2014. We have sponsored the development of excel-d, dpp, autowrap, libmir, and various other projects. We started Symmetry Autumn of Code in 2018 and hosted DConf 2019 in London.