DMD 2.076.0 Released

The core D team is proud to announce that version 2.076.0 of DMD, the reference compiler for the D programming language, is ready for download. The two biggest highlights in this release are the new static foreach feature for improved generative and generic programming, and significantly enhanced C language integration making incremental conversion of C projects to D easy and profitable.

static foreach

As part of its support for generic and generative programming, D allows for conditional compilation by way of constructs such as version and static if statements. These are used to choose different code paths during compilation, or to generate blocks of code in conjunction with string and template mixins. Although these features enable possibilities that continue to be discovered, the lack of a compile-time loop construct has been a steady source of inconvenience.

Consider this example, where a series of constants named val0 to valN needs to be generated based on a number N+1 specified in a configuration file. A real configuration file would require a function to parse it, but for this example, assume the file val.cfg is defined to contain a single numerical value, such as 10, and nothing else. Further assuming that val.cfg is in the same directory as the valgen.d source file, use the command line dmd -J. valgen.d to compile.

module valgen;
import std.conv : to;

enum valMax = to!uint(import("val.cfg"));

string genVals() 
{
    string ret;
    foreach(i; 0 .. valMax) 
    {
        ret ~= "enum val" ~ to!string(i) ~ "=" ~ to!string(i) ~ ";";
    }
    return ret;
}

string genWrites() 
{
    string ret;
    foreach(i; 0 .. valMax) 
    {
        ret ~= "writeln(val" ~ to!string(i) ~ ");";
    }
    return ret;
}

mixin(genVals);

void main() 
{
    import std.stdio : writeln;
    mixin(genWrites);
}

The manifest constant valMax is initialized by the import expression, which reads in a file during compilation and treats it as a string literal. Since we’re dealing only with a single number in the file, we can pass the string directly to the std.conv.to function template to convert it to a uint. Because valMax is an enum, the call to to must happen during compilation. Finally, because to meets the criteria for compile-time function evaluation (CTFE), the compiler hands it off to the interpreter to do so.

The genVals function exists solely to generate the declarations of the constants val0 to valN, where N is determined by the value of valMax. The string mixin on line 26 forces the call to genVals to happen during compilation, which means this function is also evaluated by the compile-time interpreter. The loop inside the function builds up a single string containing the declaration of each constant, then returns it so that it can be mixed in as several constant declarations.

Similarly, the genWrites function has the single-minded purpose of generating one writeln call for each constant produced by genVals. Again, each line of code is built up as a single string, and the string mixin inside the main function forces genWrites to be executed at compile-time so that its return value can be mixed in and compiled.

Even with such a trivial example, the fact that the generation of the declarations and function calls is tucked away inside two functions is a detriment to readability. Code generation can get quite complex, and any functions created only to be executed during compilation add to that complexity. The need for iteration is not uncommon for anyone working with D’s compile-time constructs, and in turn neither is the implementation of functions that exist just to provide a compile-time loop. The desire to avoid such boilerplate has put the idea of a static foreach as a companion to static if high on many wish lists.

At DConf 2017, Timon Gehr rolled up his sleeves during the hackathon and implemented a pull request to add support for static foreach to the compiler. He followed that up with a D Improvement Proposal, DIP 1010, so that he could make it official, and the DIP met with enthusiastic approval from the language authors. With DMD 2.076, it’s finally ready for prime time.

With this new feature, the above example can be rewritten as follows:

module valgen2;
import std.conv : to;

enum valMax = to!uint(import("val.cfg"));

static foreach(i; 0 .. valMax) 
{
    mixin("enum val" ~ to!string(i) ~ "=" ~ to!string(i) ~ ";");
}

void main() 
{
    import std.stdio : writeln;
    static foreach(i; 0 .. valMax) 
    {
        mixin("writeln(val" ~ to!string(i) ~ ");");
    }
}

Even such a trivial example brings a noticeable improvement in readability. Don’t be surprised to see compile-time heavy D libraries (and aren’t most of them?) get some major updates in the wake of this compiler release.

Better C integration and interoperation

DMD’s -betterC command line switch has been around for quite a while, though it didn’t really do much and it has languished from inattention while more pressing concerns were addressed. With DMD 2.076, its time has come.

The idea behind the feature is to make it even easier to combine both D and C in the same program, with an emphasis on incrementally replacing C code with D code in a working project. D has been compatible with the C ABI from the beginning and, with some work to translate C headers to D modules, can directly make C API calls without going through any sort of middleman. Going the other way and incorporating D into C programs has also been possible, but not as smooth of a process.

Perhaps the biggest issue has been DRuntime. There are certain D language features that depend on its presence, so any D code intended to be used in C needs to bring the runtime along and ensure that it’s initialized. That, or all references to the runtime need to be excised from the D binaries before linking with the C side, something that requires more than a little effort both while writing code and while compiling it.

-betterC aims to dramatically reduce the effort required to bring D libraries into the C world and modernize C projects by partially or entirely converting them to D. DMD 2.076 makes significant progress toward that end. When -betterC is specified on the command line, all asserts in D modules will now use the C assert handler rather than the D assert handler. And, importantly, neither DRuntime nor Phobos, the D standard library, will be automatically linked in as they normally are. This means it’s no longer necessary to manually configure the build process or fix up the binaries when using -betterC. Now, object files and libraries generated from D modules can be directly linked into a C program without any special effort. This is especially easy when using VisualD, the D plugin for Visual Studio. Not too long ago, it gained support for mixing C and D modules in the same project. The updated -betterC switch makes it an even more convenient feature.

While the feature is now more usable, it’s not yet complete. More work remains to be done in future releases to allow the use of more D features currently prohibited in betterC. Read more about the feature in Walter Bright’s article here on the D Blog, D as a Better C.

A new release schedule

This isn’t a compiler or language feature, but it’s a process feature worth noting. This is the first release conforming to a new release schedule. From here on out, beta releases will be announced on the 15th of every even month, such as 2017–10–15, 2017–12–15, 2018–2–15, etc. All final releases will be scheduled for the 1st of every odd month: 2017–11–01, 2018–01–01, 2018–03–01, etc. This will bring some reliability and predictability to the release schedule, and make it easier to plan milestones for enhancements, changes, and new features.

Get it now!

As always, the changes, fixes, and enhancements for this release can be found in the changelog. This specific release will always be available for download at http://downloads.dlang.org/releases/2.x/2.076.0, and the latest release plus betas and nightlies can be found at the download page on the DLang website.

Open Methods: From C++ to D

Prelude


Earlier this year I attended C++Now, a major conference dedicated to C++. I listened to talks given by very bright people, who used all sorts of avant-garde C++ techniques to accomplish all sorts of feats at compile time. It was a constexpr party! However, at the end of the week I had severe doubts about the future of C++.

I’ll say this for the organizers, though: they were quite open minded. They reserved the largest auditorium for a two-hour presentation of competing languages, one every day. We had Haskell and Rust, and Ali Çehreli talked about D.

I knew next to nothing about D. You see, I learned to program in Forth. Later I did some Lisp programming just for fun. To me, the idea of CTFE was natural right off the bat. So when Ali talked about static if and mixins, he definitely got my attention.

In order to learn (and evaluate) D, I decided to reproduce parts of my C++ library yomm11. It implements open multi-methods and contains code that exercises the “interesting” parts of the language, both at compile time and run time. Initially, I thought I would just see how I could reimplement bits of yomm11, how nice (or ugly) the syntax for declaring methods would turn out to be. The result was satisfying. I would even say intoxicating. I ended up bringing the port to completion and I feel that the result–openmethods.d–is the best implementation of open methods I’ve crafted so far. And it’s all done in a library, relying only on existing language features.

But wait, what are open methods?

From Member to Free

Open methods are just like virtual functions, except that they are declared outside of a class hierarchy. They are often conflated with multi-methods, because they are frequently implemented together (as is the case with this library), but really these are two different concepts. The ‘open’ part is, I believe, the more important, so I will focus more on that in this article.

Here is an example of a virtual function:

interface Animal
{
  string kick();
}

class Dog : Animal
{
  string kick() { return "bark"; }
}

class Pitbull : Dog
{
  override string kick() { return super.kick() ~ " and bite"; }
}

void main()
{
  import std.stdio : writeln;
  Animal snoopy = new Dog, hector = new Pitbull;
  writeln("snoopy.kick(): ", snoopy.kick()); // bark
  writeln("hector.kick(): ", hector.kick()); // bark and bite
}

The direct equivalent, translated to open methods, reads like this:

import openmethods;
mixin(registerMethods);

interface Animal
{
}

class Dog : Animal
{
}

class Pitbull : Dog
{
}

string kick(virtual!Animal);

@method
string _kick(Dog dog) { return "bark"; }

@method
string _kick(Pitbull dog) { return next!kick(dog) ~ " and bite"; }

void main()
{
  updateMethods();
  import std.stdio : writeln;
  Animal snoopy = new Dog, hector = new Pitbull;
  writeln("snoopy.kick(): ", snoopy.kick()); // bark
  writeln("hector.kick(): ", hector.kick()); // bark an dbite
}

Let’s break it down.

  • The string kick() in interface Animal becomes the free function declaration string kick(virtual!Animal). The implicit this parameter becomes an explicit parameter, and its type is prefixed with virtual!, thus indicating that the parameter is used to resolve calls at run time.
  • The string kick() override in class Dog becomes the free function definition @method string _kick(Dog dog) { return "bark"; }. Three things here:
    • the override is preceded by the @method attribute
    • the function name is prefixed with an underscore
    • the implicit this argument is explicitly named: Dog dog
  • The same thing happens to the override in class Pitbull, with an extra twist: super.kick() becomes next!kick(dog)
  • The calls to kick in main become free function calls – although, incidentally, they could have remained unchanged, thanks to Uniform Function Call Syntax.
  • After importing the openmethods module, a mixin is called: mixin(registerMethods). It should be called in each module that imports openmethods. It matches method declarations and overrides. It also creates a kick(Animal) function (note: sans the virtual!), which is the entry point in the dynamic dispatch mechanism.
  • Finally, main calls updateMethods. This should be done before calling any method (typically first thing in main) and each time a library containing methods is dynamically loaded or unloaded.

Open Is Good

What does it gain us? Well, a lot. Now we can add polymorphic behavior to any class hierarchy without modifying it. In fact, this implementation even allows you to add methods to Object, in a matter of speaking. Because, of course, class Object is never modified.

Let’s take a more serious example. Suppose that you have written a nifty matrix math library. Matrices come in all sorts of flavors: diagonal, shallow, tri-diagonal, and of course dense (i.e. “normal” matrices). Depending on the exact nature of a matrix, you can optimize some operations. Transposing a diagonal or a symmetric matrix amounts to returning it, unchanged. Adding sparse matrices does not require adding thousands of zeroes; and so on. And you have exploited all these properties in your matrix library, varying the behavior by means of virtual functions.

Neat.

Now let me ask you a question: should you provide a print function? A persist function?

Almost certainly not. For starters, there are many ways to display a matrix. If it is sparse, you may want to show only the non-zero elements… or all of them. You may want to display the null matrix as [0]… or in full. It is the privilige of the application to decide what matrices should look like on screen or paper. The matrix library should do the maths, and the application should do the presentation. If it needs to display matrices at all, that is. In game programming, there may be no need to display matrices. However, if you provide a print function, given the way they are implemented, the print or the persist code will always be pulled in from the library. Not good.

Now the application programmer will have to write his print and persist functions, but immediately he will be facing a problem: certainly he wants to vary the behavior according to the exact matrix type; he wants polymorphism! So he will probably end up coding a set of type switches.

Open methods solve this problem more neatly:

void print(virtual!Matrix m);

@method
void _print(Matrix m)
{
  const int nr = m.rows;
  const int nc = m.cols;
  for (int i = 0; i < nr; ++i) {
    for (int j = 0; j < nc; ++j) {
      writef("%3g", m.at(i, j));
    }
    writeln();
  }
}

@method
void _print(DiagonalMatrix m)
{
  import std.algorithm;
  import std.format;
  import std.array;
  writeln("diag(", m.elems.map!(x => format("%g", x)).join(", "), ")");
}

Accept No Visitors (c) Yuriy Solodkyy

A popular existing solution to this problem comes in the form of the Visitor pattern. Your matrix library could provide one, thus allowing the application writer to process different matrices according to their type.

In truth, Visitor is more an anti-pattern than a pattern, because the base class is aware of all its derived classes – something that flies in the face of all OOP design rules.

Here it is anyway:

import std.stdio;

interface Matrix
{
  interface Visitor
  {
    void visit(DenseMatrix m);
    void visit(DiagonalMatrix m);
  }

  void accept(Visitor v);
}

class DenseMatrix : Matrix
{
  void accept(Visitor v) { v.visit(this); }
}

class DiagonalMatrix : Matrix
{
  void accept(Visitor v) { v.visit(this); }
}

class PrintVisitor : Matrix.Visitor
{
  this(File of) { this.of = of; }

  void visit(DenseMatrix m) { of.writeln("print a DenseMatrix"); }
  void visit(DiagonalMatrix m) { of.writeln("print a DiagonalMatrix"); }

  File of;
}

void main()
{
  Matrix dense = new DenseMatrix, diagonal = new DiagonalMatrix;
  auto printer = new PrintVisitor(stdout);
  dense.accept(printer);
  diagonal.accept(printer);
}

This approach is more verbose than using an open method, and it has a more fatal flaw: it is not extensible. Suppose that the user of your matrix library wants to add matrices of his own design. For example, a SparseMatrix. The Visitor will be of no help here. With open methods, on the other hand, the solution is available, simple, and elegant:

// from library

void print(virtual!Matrix m, File of);

@method
void _print(DenseMatrix m, File of)
{
  of.writeln("print a DenseMatrix");
}

@method
void _print(DiagonalMatrix m, File of)
{
  of.writeln("print a DiagonalMatrix");
}

// extend library

class SparseMatrix : Matrix
{
  // ...
}

@method
void _print(SparseMatrix m, File of)
{
  of.writeln("print a SparseMatrix");
}

Multiple Dispatch

Occasionally, there is a need to take into account the type of two or more arguments to select the appropriate behavior. This is called multiple dispatch. Most languages only support single dispatch in the form of virtual member functions. Once again, the “solution” involves type switches or visitors. A few languages address this situation directly by means of multi-methods. The most notorious example is the Common Lisp Object System. Recently, a string of new languages have native support for multi-methods: Clojure (unsurprising for a lispoid), Julia, Nice, Cecil, TADS (a language for developing text-based adventure games).

This library implements multi-methods as well. There is no limit to the number of arguments that can be adorned with the virtual! qualifier. They will all be considered during dynamic dispatch.

Continuing the matrix library example, you probably want to provide binary operations on matrices: addition, subtraction and multiplication. If both operands are matrices, you really want to pick the right algorithm depending on the respective types of both operands. There is no point wasting time on adding all the elements if both operands are diagonal matrices; adding the diagonals suffices. Crucially, adding two DiagonalMatrix objects should return a DiagonalMatrix, not a plain DenseMatrix. Adding a DiagonalMatrix and a TriDiagonalMatrix should return a TriDiagonalMatrix, etc.

With open multi-methods, there is no problem at all:

module matrix;

Matrix plus(virtual!Matrix, virtual!Matrix);

module densematrix;

@method
Matrix _plus(Matrix a, Matrix b)
{
  // fallback: add all elements, fetched via interface
  // return a DenseMatrix
}

@method
Matrix _plus(DenseMatrix a, DenseMatrix b)
{
  // add all elements, access representation directly
  // return a DenseMatrix
}

module diagonalmatrix;

@method
Matrix _plus(DiagonalMatrix a, DiagonalMatrix b)
{
  // just add the elements on diagonals
  // return a DiagonalMatrix
}

Once again, open methods make the library extensible. It is trivial to plug new types in:

module mymatrices;

@method
Matrix _plus(SparseMatrix a, SparseMatrix b)
{
  // just add the non-zero elements
  // return a SparseMatrix
}

@method
Matrix _plus(SparseMatrix a, DiagonalMatrix b)
{
  // still don't add all the zeroes
  // return a SparseMatrix
}

@method
Matrix _plus(DiagonalMatrix a, SparseMatrix b)
{
  return plus(b, a); // matrix addition is commutative
}

Implementation Notes and Performance

This implementation uses tables of pointers to select the appropriate function to call. The process is very similar to what happens when a regular, virtual member function is called.

Each class involved in method dispatch–either because it is used as a virtual argument in a method declaration, or because it inherits from a class or an interface used as a virtual argument–has an associated method table (mtbl). The pointer to the method table (mptr) associated to a given class is stored, by default, in the deallocator pointer of the class’s ClassInfo. The first entry in a class’s vtable contains a pointer to its ClassInfo. The deallocator pointer was used to implement the deprecated delete method, so it is reasonable to recycle it. The deallocator pointer may be removed some day, or one may want to use methods in conjunction with classes that implement delete, so an alternative is supported. Tagging a method with @mptr("hash") makes it fetch the method table pointer from an array indexed by a perfect integer hash calculated during updateMethods. In this case, finding the mptr amounts to multiplying the vptr’s value by an integer and applying a bit mask.

The method table contains one entry for each virtual parameter for each method. If the method has a single virtual argument, the entry contains the specialization’s address, just like an ordinary virtual function; otherwise, the entry contains a pointer to a row in a multi-dimensional dispatch table for the first argument, and integer indexes for the subsequent virtual arguments.

Since the set of methods applicable to a given class is known only at run time and may change in the presence of dynamic loading, the position of a method’s entries in the method table is not fixed; it is stored in a table associated with each method. Finally, in the presence of multiple dispatch, a per-method array of strides is used to convert the multi-dimensional index to a linear offset.

However, finding the specialization amounts to a few memory reads, additions and perhaps multiplications. As a result, open methods are almost as fast as virtual functions backed by the compiler. How much slower they are depends on several factors, including the compiler, or whether the call is issued from an interface or a class. The following table sums up some of my benchmarks. Rows come in groups of three: the “usual”, compiler-supported virtual member functions; the functional equivalent using open methods; and the cost, expressed as (method - virtual) / virtual:

mptr in deallocator dmd ldc2 gdc
vfunc (interface) 1.84 1.80 1.80
vs 1-method (interface) 10.73 3.53 6.05
delta% 484% 96% 236%
vfunc (class) 1.83 1.80 1.80
vs 1-method (class) 5.12 2.13 1.80
delta% 180% 18% 0%
double dispatch 4.11 2.40 2.13
2-method 7.75 3.14 3.40
delta% 88.45% 30.71 59.85

Times in nanoseconds, measured on my Asus ROG G751JT.

A few results stand out. The first is expected, the others are quite remarkable.

  1. gdc and ldc2 do a better job at optimizing method dispatch
  2. Method calls that take an object perform much better than those taking an interface; there may be some further improvements to be done here.
  3. Method calls from an object are almost as fast as plain virtual function calls when ldc2 is used; they are just as fast with gdc. The latter is surprising and calls for further investigation.
  4. Disappointingly, double dispatch beats binary methods. This is not the case in C++. My intuition is that extracting the method table pointer requires traversing too many indirections, to the point that it is more costly than a plain virtual function call. In contrast, yomm11 sticks the mptr right inside the object (but at the cost of requiring changes to the classes). This deserves further investigation, but I am convinced that a bit of help from the compiler (like reserving the second element of the vtbl for the mptr) would reverse this result.

Memory footprint is also a common concern when implementing table-based multiple dispatch: imagine a method with three virtual arguments, which can each be any of a dozen classes. This gives us a 12x12x12 table, containing 1728 function pointers. Fortunately, it is rare that a specialization is defined for each combination of arguments. Typically, there is a lot of duplication along each axis. This implementation takes advantages of this: it builds tables free of redundancies. The table is not “compressed” per se, as it never exists as a cartesian product of all the class sets; rather, it is built in terms of class partitions, not classes, where all the classes in the same group in the same dimension have the same set of candidate specializations. See
this article for an example.

Extending the Language – in D and in C++

Yomm11, the initial implementation of open methods in C++, takes 1845 lines of code (excluding comments) to implement; the D version weighs 1120 lines. Much of the difference is due to D’s ClassInfo. It contains information on the base class and inherited interfaces. It is used to build a bi-directional inheritance graph of the types that have methods attached to them.

C++’s type_info contains no such informaton, thus yomm11 comes with its own runtime class information system, and a macro that the user must call for each class participating in method dispatch. The usual difficulties with static constructors arise, and necessitates extra code to handle them.

Yomm11 can be used in two modes: intrusive and orthogonal. In the intrusive mode, the user augments the classes using macro calls. One of them allocates a method table pointer in the object; the other–called in each constructor–initializes the method pointer. In the orthogonal mode, no modification of the classes is required: the method pointer is stored in a hash map keyed by the type_info obtained via the typeid operator.

openmethods.d has two modes, too, but they are both orthogonal. The default mode stores the method pointer in the deallocator field of the ClassInfo. The ClassInfo of an object is available as the first pointer of the virtual function table; all this is documented. However, hijacking deallocator is a bit like cheating, and nothing guarantees that that field will be there forever.

For that reason, the library supports another mode, which is only slightly slower than the first: store the method pointer in an array indexed by a perfect integer hash of the virtual table pointer.

Unfortunately, it is not possible to use this approach in C++. It is possible to retrieve an object’s vptr, albeit by resorting to undocumented implementation details. However, the library needs to build the method tables without having instances of objects at hand; in D, on the other hand, the value of the vptr is available in the ClassInfo. Another idea would be to use a pointer to the type_info structure; alas, while a type_info can be obtained from a type as well as from an object, the standard explicitly states that the type_info object for a given type may not be unique.

Thus D provides at bit more information than C++, and that makes all the difference.

As for the meta-programming involved in processing the method declarations and specializations, it is easier, and yields a better syntax, in D than in C++, for several reasons.

Obviously, constructs like static if and foreach on type tuples make meta-programming easier. But the real advantage of D comes from the interplay
of template mixins, string mixins, compile-time reflection and alias. The mixin(registerMethods) incantation scans the entire translation unit and:

  • locates all the method declarations by detecting the functions that have virtual! in their signature
  • creates (via an alias created by a string mixin) a function with the same signature, minus the virtual qualifiers, which is what the user calls
  • finds all the method specializations (by locating the functions that have a @method attribute) and generates code that, at runtime, will register the specializations with the appropriate method

Conclusion

Object-oriented programming became popular in the nineties, but has been subjected to a lot of criticism in the last decade. This is in part because OOP promised modularity and extensibility, but failed to deliver. Instead we got “God” classes and Visitors. It is not the fault of the OOP paradigm per se, but rather of the unnatural and unnecessay fusion of class membership and polymorphism that most OO languages enforce. Open methods correct this mistake. As a bonus, this implementation also supports multiple dispatch. This is OOP done right: not objects “talking” to each other, but applying the appropriate algorithm depending on the arguments’ runtime types.

Open methods can be implemented as a library in C++ and in D, but D has a clear edge when it comes to meta-programming. As a result, the D version of the library delivers a lighter, cleaner syntax.

openmethods.d is available on dub

Jean-Louis Leroy is not French, but Belgian. He got his first taste of programming from a HP-25 calculator. His first real programming language was Forth, where CTFE is pervasive. Later he programmed (a little) in Lisp and Smalltalk, and (a lot) in C, C++, and Perl. He now works for Bloomberg LP in New York. His interests include object-relational mapping, open multi-methods, DSLs, and language extensions in general.

D as a Better C

Walter Bright is the BDFL of the D Programming Language and founder of Digital Mars. He has decades of experience implementing compilers and interpreters for multiple languages, including Zortech C++, the first native C++ compiler. He also created Empire, the Wargame of the Century. This post is the first in a series about D’s BetterC mode

D was designed from the ground up to interface directly and easily to C, and to a lesser extent C++. This provides access to endless C libraries, the Standard C runtime library, and of course the operating system APIs, which are usually C APIs.

But there’s much more to C than that. There are large and immensely useful programs written in C, such as the Linux operating system and a very large chunk of the programs written for it. While D programs can interface with C libraries, the reverse isn’t true. C programs cannot interface with D ones. It’s not possible (at least not without considerable effort) to compile a couple of D files and link them in to a C program. The trouble is that compiled D files refer to things that only exist in the D runtime library, and linking that in (it’s a bit large) tends to be impractical.

D code also can’t exist in a program unless D controls the main() function, which is how the startup code in the D runtime library is managed. Hence D libraries remain inaccessible to C programs, and chimera programs (a mix of C and D) are not practical. One cannot pragmatically “try out” D by add D modules to an existing C program.

That is, until Better C came along.

It’s been done before, it’s an old idea. Bjarne Stroustrup wrote a paper in 1988 entitled “A Better C“. His early C++ compiler was able to compile C code pretty much unchanged, and then one could start using C++ features here and there as they made sense, all without disturbing the existing investment in C. This was a brilliant strategy, and drove the early success of C++.

A more modern example is Kotlin, which uses a different method. Kotlin syntax is not compatible with Java, but it is fully interoperable with Java, relies on the existing Java libraries, and allows a gradual migration of Java code to Kotlin. Kotlin is indeed a “Better Java”, and this shows in its success.

D as Better C

D takes a radically different approach to making a better C. It is not an extension of C, it is not a superset of C, and does not bring along C’s longstanding issues (such as the preprocessor, array overflows, etc.). D’s solution is to subset the D language, removing or altering features that require the D startup code and runtime library. This is, simply, the charter of the -betterC compiler switch.

Doesn’t removing things from D make it no longer D? That’s a hard question to answer, and it’s really a matter of individual preference. The vast bulk of the core language remains. Certainly the D characteristics that are analogous to C remain. The result is a language somewhere in between C and D, but that is fully upward compatible with D.

Removed Things

Most obviously, the garbage collector is removed, along with the features that depend on the garbage collector. Memory can still be allocated the same way as in C – using malloc() or some custom allocator.

Although C++ classes and COM classes will still work, D polymorphic classes will not, as they rely on the garbage collector.

Exceptions, typeid, static construction/destruction, RAII, and unittests are removed. But it is possible we can find ways to add them back in.

Asserts are altered to call the C runtime library assert fail functions rather than the D runtime library ones.

(This isn’t a complete list, for that see http://dlang.org/dmd-windows.html#switch-betterC.)

Retained Things

More importantly, what remains?

What may be initially most important to C programmers is memory safety in the form of array overflow checking, no more stray pointers into expired stack frames, and guaranteed initialization of locals. This is followed by what is expected in a modern language — modules, function overloading, constructors, member functions, Unicode, nested functions, dynamic closures, Compile Time Function Execution, automated documentation generation, highly advanced metaprogramming, and Design by Introspection.

Footprint

Consider a C program:

#include <stdio.h>

int main(int argc, char** argv) {
    printf("hello world\n");
    return 0;
}

It compiles to:

_main:
push EAX
mov [ESP],offset FLAT:_DATA
call near ptr _printf
xor EAX,EAX
pop ECX
ret

The executable size is 23,068 bytes.

Translate it to D:

import core.stdc.stdio;

extern (C) int main(int argc, char** argv) {
    printf("hello world\n");
    return 0;
}

The executable size is the same, 23,068 bytes. This is unsurprising because the C compiler and D compiler generate the same code, as they share the same code generator. (The equivalent full D program would clock in at 194Kb.) In other words, nothing extra is paid for using D rather than C for the same code.

The Hello World program is a little too trivial. Let’s step up in complexity to the infamous sieve benchmark program:

#include <stdio.h>

/* Eratosthenes Sieve prime number calculation. */

#define true    1
#define false   0
#define size    8190
#define sizepl  8191

char flags[sizepl];

int main() {
    int i, prime, k, count, iter;

    printf ("10 iterations\n");
    for (iter = 1; iter <= 10; iter++) {
        count = 0;
        for (i = 0; i <= size; i++)
            flags[i] = true;
        for (i = 0; i <= size; i++) {
            if (flags[i]) {
                prime = i + i + 3;
                k = i + prime;
                while (k <= size) {
                    flags[k] = false;
                    k += prime;
                }
                count += 1;
            }
        }
    }
    printf ("\n%d primes", count);
    return 0;
}

Rewriting it in Better C:

import core.stdc.stdio;

extern (C):

__gshared bool[8191] flags;

int main() {
    int count;

    printf("10 iterations\n");
    foreach (iter; 1 .. 11) {
        count = 0;
        flags[] = true;
        foreach (i; 0 .. flags.length) {
            if (flags[i]) {
                const prime = i + i + 3;
                auto k = i + prime;
                while (k < flags.length) {
                    flags[k] = false;
                    k += prime;
                }
                count += 1;
            }
        }
    }
    printf("%d primes\n", count);
    return 0;
}

It looks much the same, but some things are worthy of note:

  • extern (C): means use the C calling convention.
  • D normally puts static data into thread local storage. C sticks them in global storage. __gshared accomplishes that.
  • foreach is a simpler way of doing for loops over known endpoints.
  • flags[] = true; sets all the elements in flags to true in one go.
  • Using const tells the reader that prime never changes once it is initialized.
  • The types of iter, i, prime and k are inferred, preventing inadvertent type coercion errors.
  • The number of elements in flags is given by flags.length, not some independent variable.

And the last item leads to a very important hidden advantage: accesses to the flags array are bounds checked. No more overflow errors! We didn’t have to do anything
in particular to get that, either.

This is only the beginning of how D as Better C can improve the expressivity, readability, and safety of your existing C programs. For example, D has nested functions, which in my experience work very well at prying goto’s from my cold, dead fingers.

On a more personal note, ever since -betterC started working, I’ve been converting many of my old C programs still in use into D, one function at a time. Doing it one function at a time, and running the test suite after each change, keeps the program in a correctly working state at all times. If the program doesn’t work, I only have one function to look at to see where it went wrong. I don’t particularly care to maintain C programs anymore, and with -betterC there’s no longer any reason to.

The Better C ability of D is available in the 2.076.0 beta: download it and read the changelog.

On Tilix and D: An Interview with Gerald Nunn

Joakim is the resident interviewer for the D Blog. He has also interviewed members of the D community for This Week in D and is responsible for the Android port of LDC.

Gerald Nunn is the developer of Tilix (formerly called Terminix), an advanced, open-source, Gtk3-based tiling terminal emulator, which is the most-starred D project on github, recently surpassing even the D reference compiler, DMD. Earlier this year at DConf in Berlin, he talked about how he chose D. His slides and a recorded video are available. In his day job, which has nothing to do with desktop GUI applications, he is a Senior Middleware Solutions Architect at Red Hat.

Joakim: What is a tiling terminal emulator?

Gerald: A tiling terminal emulator allows you to split the terminal into multiple tiles and rearrange them in any layout that makes the most sense for the particular task you are working on. People that work in multiple terminals simultaneously typically find them the most useful, particularly with ever-increasing monitor sizes and resolutions.

While the tiling is cool, the primary reason I created Tilix was that I wanted a terminal emulator that followed the Gnome Human Interface Guidelines (HIG) and used Client-Side Decorations (CSD). Tilix follows the Gnome HIG published here, which means adhering to spacing, layout and other recommendations. Following the HIG is important in order for your application to be consistent with the desktop experience as a whole.

CSD refers to the titlebar of the window, where the client assumes responsibility for it, rather than the display manager, and can populate the titlebar with buttons and other controls. This is part of the Gnome HIG and most default Gnome applications (gedit, files, videos, etc.) use this. The one exception is gnome-terminal which does not use the CSD at all.

Joakim: Can you give some examples of how you conform better to the Gnome HIG?

Gerald: The Gnome HIG specifies a specific design language with regards to how applications running in Gnome should look and feel. Some examples of Tilix following the HIG include the use of CSDs and application menus, following various guidelines such as spacing and layouts, etc. Additionally, the Gnome designers have put together a variety of mockups of how they think various applications should look. Tilix uses the mockups developed by Gnome designers for the terminal where feasible. For example, in Tilix the preferences and profiles dialog used to be separate, however one of the Gnome designers proposed this mockup for gnome-terminal. I went ahead and implemented it in Tilix, for a much better experience than what I had previously.

As a result of using CSDs and following the Gnome HIG, hopefully the experience of using Tilix in Gnome feels more organic to its users.

The interesting thing is the tension between people who use Tilix on Gnome and those who use it on other distributions. While I make no bones about the fact that Gnome is my primary target, I do try to make the experience better in other desktop environments by allowing the user to disable the CSD in favor of a normal titlebar if they so wish.

Joakim: You come to D from primarily a Java background. Do you still write Java-style code in D? Was that easy, i.e. how much did you have to change to write D instead?

Gerald: Yes, my background is in Java. I found it quite interesting at DConf when I asked how many people came from a non C/C++ background that only one other fellow raised his hand.

If you look at my code in Tilix, it does look a lot like Java code. Some of that is due to my background and some is due to GtkD being a class-based wrapper. I find switching between D and Java to be pretty seamless for the most part; there is much less cognitive friction between the two than say, switching between Java and Python.

The biggest D idiom I had to learn was ranges, since it is a pretty foundational feature of D. However that wasn’t overly complicated. Compile-Time Function Evaluation (CTFE) is still something that doesn’t come naturally. I have to look it up again every time I need to use it and my current attempts at CTFE from a code perspective are pretty ugh. I’d like to leverage CTFE more in Tilix as I continue to get more experience with D.

Finally, my lack of C experience means that the other area I struggle with a bit is interfacing with C code. While for the most part it’s pretty straightforward, when I have to start deciphering something complex, it gets a bit hoary. Support for flatpack is currently being held up, as I haven’t summoned up the mental energy to work through some of the issues I am having interfacing with C code.

Having said that, I do have some experience with native code development, as I spent a fair amount of time writing code in Delphi and Object Pascal many, many years ago. This is where most of my GUI experience comes from as well.

Joakim: From your DConf talk, you’re obviously not worried much about the Garbage Collector (GC). Have you had to think about it at all when you’re coding Tilix? Any issues with the GC causing stuttering in the GUI?

Gerald: Coming from Java, the GC is pretty natural for me and I definitely do not consider it a negative for D. I think the GC in D gets a lot of bad press based on Java experiences, but it’s important to remember that the GC in D is quite different than the one in Java. The biggest difference to me is that you have more options to control the GC, since it’s well understood when it can kick off a GC cycle. I’ve been very happy to see more people pushing back on the various reddit threads and forum posts complaining about the use of GC.

I haven’t had any issues with the GC in terms of pauses, and no Tilix users have opened cases about it. I have had a few GC-related issues, primarily around leaking memory due to holding references, but these have all been programmer error rather than an issue with D’s implementation of GC. I have another GTK D application, Visual Grep, where I ran into abysmal performance when loading a large amount of matches in a tight loop. However, simply disabling the GC for that section of the code sped things up immensely.

Joakim: The github repository for Tilix is remarkably clean, no open Pull Requests (PRs) and a low percentage of issues still open. How much time do you spend weekly on Tilix? Is Tilix strictly a hobby or has it become something more?

Gerald: Tilix is strictly a hobby. I probably spend 5 to 10 hours a week on it. At this point it’s a mature application, hence the relatively low number of issues. I also prioritize fixing bugs over adding new features, which helps keep the list manageable.

With regards to pull requests, I’m a firm believer in being responsive, so I will typically respond to a PR within a day or two. As a contributor myself, I know nothing kills interest like seeing your PR languish for weeks, months, or even years. If you want people to contribute, which I definitely do, then I feel you owe contributors the courtesy of responding in a timely fashion.

Now, Tilix is a relatively small project so it’s easy for me to adopt that approach. I can understand why larger projects may have more difficulty in this area.

Joakim: D has a lot of features, how well do you know it? You mentioned that you want to use some of the compile-time capabilities more: which D features do you think Tilix would benefit from in the future and how?

Gerald: I don’t think I know it that well to be honest, beyond the core set of features I use in Tilix. I’m always amazed at the guys on the forum that can argue the merits/drawbacks of the low-level details of the language. That’s definitely not me. I’d like to get better at it, but the reality is that I only use D as a hobby so I can’t invest the same amount of time I do with Java. In addition, my job at Red Hat has more of an infrastructure component than my previous jobs, so a lot of my learning time is spent ramping up on that side of the house.

In terms of features that Tilix would benefit from, I think using CTFE and ranges would be very beneficial for exposing some of the capabilities in GtkD in a more idiomatic way. I have a fair amount of code where it could be a lot more concise with appropriate usage of CTFE. As a simple example, using ranges to support iterating over various artifacts using foreach rather than a classical for loop. However, I think some of the more complex use cases, like supporting D-Bus and GObject, would be very useful.

For those not familiar with GObject, it’s the base level object in GTK and is reference-counted. Being able to create GObjects easily in D, like you can in Python, would make it much easier to interface with some of the APIs; right now doing so is the equivalent of writing it in raw C and it’s somewhat laborious. Mike Wey, the GtkD maintainer, has started doing some work on this.

Joakim: You mentioned at DConf that D has a quick edit-compile cycle: how do you enable that, i.e. what IDE, compilers, toolchain do you use, both for development and then for release?

Gerald: I use MS Visual Studio Code on Linux with the excellent code-d plugin, written by Jan “WebFreak” Jurzitza. It gives me all of the features I need (code completion, tooltip hints, etc.). The only thing I find missing from Java IDEs is a refactoring capability. For development, I use DMD as it has the fastest compile time. Release builds are done using LDC, the D compiler with an LLVM backend, since it generates a smaller and faster binary. I rarely need to fire up a debugger, but when I do I just use GDB from the command-line.

Joakim: What problems have you had with D? What features do you dislike?

Gerald: No major problems from my perspective. I’m generally very happy with the language and find it strikes the right balance between ease of use and capabilities. Most of my dislikes would be centered on the standard library, Phobos, rather than the language itself, and those dislikes directly correlate to lack of manpower.

No major issues with Phobos, but rather a bunch of irritants. For example, you cannot easily use immutable with send in std.concurrency, std.experimental.logger is still experimental, the json parser has issues if localization is set to use a comma instead of a period for decimals, etc., etc. None of them in and of themselves are showstoppers, and most of these are really about having the manpower to polish things up. I’m actually somewhat reluctant to complain about them because I could probably fix some of them myself and submit PRs.

I tend to get more annoyed about the negativity in the forums with regards to GC. I do feel that sometimes people get so wrapped up in what D needs for it to be a perfect systems language (i.e. no GC, memory safety, etc.), it gets overlooked that it is a very good language for building native applications as it is now. While D is often compared to Rust, in some ways the comparison to Go is more interesting to me. Both are GC-based languages and both started as systems languages, however Go pivoted and doubled down on the GC and has seen success. One of the Red Hat products I support, OpenShift, leverages Kubernetes (a Google project) for container orchestration and it’s written in Go.

I think D as a language is far superior to Go, and I wish we would toot our horn a little more in this regard instead of the constant negative discussion around systems programming. Now in fairness, Go has a large corporate sponsor whereas D does not, however the contrast in positioning is still interesting to me.

Joakim: What are your future plans for Tilix?

Gerald: The two biggest features I’d like to add is support for tmux control mode and adding the ability for the popout sidebar to be permanently displayed.

For those not familiar with tmux, it is a terminal multiplexer; it essentially does terminal tiling, but within the terminal itself. It also supports a number of other features, but the most interesting is keeping terminal sessions alive outside of the terminal. Since it does tiling within the terminal, there is a bit of a performance hit, plus from a GUI perspective it can’t leverage native widgets like scrollbars. To mitigate this, it supports something called control mode that enables it to integrate with a tiling terminal emulator, so that spawning of new terminals, i.e. tiling, is managed outside of tmux. This greatly improves its performance, while allowing users to leverage other features that tmux supports. At the moment, only iterm2 on OSX supports this AFAIK.

The sidebar in Tilix is one of the more controversial UI elements. I opted not to implement a tabbed interface, because I found them useless in terms of figuring out which tab I wanted; there simply isn’t enough room on the tab to distinguish them and manually renaming them is a pain. The sidebar is my attempt at an alternative, it renders a thumbnail of each session (aka tab) in a sidebar that can be popped out as needed to switch between sessions.

The Tilix sidebar in action.

Some people have a strong preference for something that is permanently available, unfortunately making the sidebar always visible isn’t easy, as generating the thumbnails takes a significant amount of time due to the way GTK is structured. There are potentially ways to make it work, but there is a time investment required to try different options to see what is feasible and then what is the most effective.

In terms of a dream feature, I’d love to switch to using a terminal emulator that is written natively in D, rather than the GTK VTE (Virtual Terminal Emulator) that is written in C that I’m using now. For those not familiar with it, the VTE is the terminal emulation widget used by Gnome Terminal and is available as a reusable widget. Many terminal emulators in Linux use this widget (gnome-terminal, guake, terminator, tilix, etc.), as it provides a production-ready emulator that has been through a huge amount of testing.

The downside with using it is that any custom features you want to implement that involve the actual terminal emulation layer require modifying the VTE and getting those modifications in upstream. I have a few patches that Tilix supports (for triggers and badges), but frankly I’ve done a poor job of getting them into upstream. Part of the reason for this is VTE is written in C and getting up to speed enough with C to make a quality patch is time-consuming.

So having the terminal emulation written in D would make this much easier, however it’s a huge time investment as people underestimate the amount of work involved. There’s a lot of edge cases in terminal emulation, plus adding all of the stuff to make it user-friendly (search, clicking links, etc.), it’s a lot to take on. I simply don’t have the time to make this a reality unless I win the lottery. If someone wanted to take that part on and create a GTK terminal emulation widget that has all of the needed features and stick with it for the long haul, I’d be happy to work with them to get it integrated with Tilix. Adam Ruppe has already created one that works quite well from my testing of it; if someone wanted to work on converting it to a GTK widget, add the necessary improvements and agree to maintain it, feel free to ping me. 🙂

Joakim: Please take us from your experience first discovering and using D to writing Tilix.

Gerald: Throughout my working career, I’ve always had hobby projects going on. Some things I worked on included a popular add-in for Delphi called Gexperts, a Windows file explorer replacement, a Java IDE called Gel and a popular Android app called OnTrack Diabetes that I sold a few years ago. A couple of years ago, I was looking for something new to work on as my hobby program and settled on the idea of building a desktop application for Linux. I knew it needed to use the GTK toolkit, since Gnome is my preferred desktop environment, and particularly with my past Delphi experience in GUIs which I could leverage. I also knew I wasn’t interested in coding in C or C++, so I had a look at what the alternatives were.

I started with Python, as it has excellent support for GTK and I had done a bit of Jython programming in WebLogic, as the Weblogic Scripting Tool (WLST) used it. However, most of my previous work had been small scripts and I quickly realized that at larger scales Python wasn’t for me. I really prefer statically-typed languages in general and Python’s dynamic typing drove me batty, particularly on a hobby program where I constantly needed to rebuild my mental stack since I worked on it infrequently.

I also looked at Rust and Go, but at the time neither of them had feature-complete GTK bindings. Also, while Rust had a lot of positive press, it had a formidable learning curve and I was less than convinced that its focus on memory safety via the borrow checker was a better approach than GC.

I was aware of D, as I had looked at D previously many years ago and liked the language, but the ecosystem was so weak it just wasn’t that useful for practical work. I gave it a second look and found that it had greatly improved and amazingly, full GtkD bindings were available. It also helped that D and Java are similar enough that picking up D was incredibly easy. Once I learned how ranges worked, it was easy to start cranking out code.

I first created a small application called Visual Grep that wraps grep into a GUI. When I was consulting, I often had to grep large code bases looking for specific patterns and a GUI that made browsing the matches was an absolute necessity. This was a great first application, as I learned quite a few things about D. The application performance was initially shit with large result sets, because the GC was constantly kicking in when loading results due to the constant allocation. Disabling the GC during that tight loop improved the performance immeasurably. I also learned about integrating GTK with D’s multi-threading capabilities.

I was inspired by the UI from Gnome Builder, an IDE, and thought that it would work quite well for a terminal emulator. Thus Tilix was born. Well, actually at first it was called Terminix, but once it started getting popular I received a polite cease-and-desist order from Terminix, the American pest control company. Being Canadian, I wasn’t overly aware of them, hence why I didn’t think too much about the name. Lesson learned: spend time choosing a good name in case your app does become more popular than you expect.

I’ve enjoyed my time with D and it’s been a great language for creating desktop applications in GTK. In many ways, I feel like D is a natural successor to Vala, which was a language created specifically for building GTK applications but has been slowly dying, largely due to its single focus. If you are not building GTK apps, you aren’t using Vala, which means the pool of people using it and working on it is by definition very small.

I also feel that D is a natural successor to Delphi, at least with GtkD, as a potent tool for creating desktop applications. With its fast compile time and as an easy-to-learn language, it brings many of Delphi’s best attributes into the modern age. Plus I can type { and } instead of Begin and End and increase my efficiency by 75% or so. 🙂

A DUB Case Study: Compiling DMD as a Library

In his day job, Jacob Carlborg is a Ruby backend developer for Derivco Sweden, but he’s been using D on his own time since 2006. He is the maintainer of numerous open source projects, including DStep, a utility that generates D bindings from C and Objective-C headers, DWT, a port of the Java GUI library SWT, and DVM, the topic of another post on this blog. He implemented native Thread Local Storage support for DMD on OS X and contributed, along with Michel Fortin, to the integration of Objective-C in D.

DUB is the official build tool and package manager for the D programming language. Originally written and currently maintained by Sönke Ludwig as part of the vibe.d web framework, its acceptance as an official part of the D toolchain means it is now shipping with the most recent DMD and LDC compilers.

A Quick Introduction to DUB

If you have have the latest DMD or LDC installed, you already have DUB installed as well. If not, or if you want to check for a more recent version, you can get the very latest release, beta or release candidate from the DUB download page.

You can create a new DUB project by executing the dub init command. This will start an interactive setup that guides you through project creation.

  1. First decide the format of the package recipe. Two formats are supported: JSON and SDLang. Here we picked SDLang.
  2. Then specify the name of the project. Press enter to use the default name, which is displayed in brackets and is inferred from the directory
  3. Do the same for the description, author, license, copyright, and dependencies to select the default values
$ dub init foo
Package recipe format (sdl/json) [json]: sdl
Name [foo]:
Description [A minimal D application.]:
Author name [Jacob Carlborg]:
License [proprietary]:
Copyright string [Copyright © 2017, Jacob Carlborg]:
Add dependency (leave empty to skip) []:
Successfully created an empty project in '/Users/jacob/tmp/foo'.
Package successfully created in foo

After the setup has completed, the following files and directories will have been created:

$ tree foo
foo
├── dub.sdl
└── source
    └── app.d

1 directory, 2 files
  • dub.sdl is the package recipe file, which provides instructions telling DUB how to build the package
  • source is the default path where DUB looks for D source files
  • app.d contains the main function and is an example Hello World generated by DUB with the following content:
import std.stdio;

void main()
{
	writeln("Edit source/app.d to start your project.");
}

The content of the dub.sdl file is the following:

name "foo"
description "A minimal D application."
authors "Jacob Carlborg"
copyright "Copyright © 2017, Jacob Carlborg"
license "proprietary"

All of which was taken from what we specified during project creation. By default, DUB looks for D source files in either source or src directories and compiles all files it finds there and in any subdirectories.

To build and run the application, navigate to the project’s root directory, foo in this case, and invoke dub:

$ dub
Performing "debug" build using dmd for x86_64.
foo ~master: building configuration "application"...
Linking...
Running ./foo
Edit source/app.d to start your project.

To build without running, invoke dub build:

$ dub build
Performing "debug" build using dmd for x86_64.
foo ~master: building configuration "application"...
Linking...

Case Study: DMD as a Library

Recently there has been some progress in making the D compiler (DMD) available as a library. Razvan Nitu has been working on it as part of his D Foundation scholarship at the University Politechnica of Bucharest. He gave a presentation at DConf 2017 (a video of the talk is available, as well as examples in the DMD repository). So I had the idea that as part of the DConf 2017 hackathon I could create a simple DUB package for DMD to make only the lexer and the parser available as a library, something his work has made possible.

Currently DMD is built using make. There are three Makefiles, one for Posix, one for 32-bit Windows and one for 64-bit Windows  (which is only a wrapper of the 32-bit one). I don’t intend to try to completely replicate the Makefiles as a DUB package (they contain some additional tasks besides building the compiler), but instead will start out fresh and only include what’s necessary to build the lexer and parser.

DMD already has all the source code in the src directory, which is one of the directories DUB searches by default. If we would leave it as is, DUB would include the entirety of DMD, including the backend and other parts we don’t want to include at this point.

The first step is to create the DUB package recipe file. We start simple with only the metadata (here using the SDLang format):

name "dmd"
description "The DMD compiler"
authors "Walter Bright"
copyright "Copyright © 1999-2017, Digital Mars"
license "BSL-1.0"

When we have this we need to figure out which files to include in the package. We can do this by invoking DMD with the -deps flag to generate the imports of a module. A good start is the lexer, which is located in src/ddmd/lexer.d. We run the following command to output the imports that lexer.d is using:

$ dmd -deps=deps.txt -o- -Isrc src/ddmd/lexer.d

This will write a file named deps.txt containing all the imports used by lexer.d. The -o- flag is used to tell the compiler not to generate any code. The -I flag is used to add an import path where the compiler will look for additional modules to import (but not compile). An example of the output looks like this (the long path names have been reduced to save space):

core.attribute (druntime/import/core/attribute.d) : private : object (druntime/import/object.d)
object (druntime/import/object.d) : public : core.attribute (druntime/import/core/attribute.d):selector
ddmd.lexer (ddmd/lexer.d) : private : object (druntime/import/object.d)
core.stdc.ctype (druntime/import/core/stdc/ctype.d) : private : object (druntime/import/object.d)
ddmd.root.array (ddmd/root/array.d) : private : object (druntime/import/object.d)
ddmd.root.array (ddmd/root/array.d) : private : core.stdc.string (druntime/import/core/stdc/string.d)

The most interesting part of this output, in this case, is the first column, which consists of a long list of module names. What we are interested in here is a unique list of modules that are located in the ddmd package. All modules in the core package are part of the D runtime and are already precompiled as a library and automatically linked when compiling a D executable, so these modules don’t need to be compiled. The modules from the ddmd package can be extracted with some search-and-replace in your favorite text editor or using some standard Unix command lines tools:

$ cat deps.txt | cut -d ' ' -f 1 | grep ddmd | sort | uniq
ddmd.console
ddmd.entity
ddmd.errors
ddmd.globals
ddmd.id
ddmd.identifier
ddmd.lexer
ddmd.root.array
ddmd.root.ctfloat
ddmd.root.file
ddmd.root.filename
ddmd.root.hash
ddmd.root.outbuffer
ddmd.root.port
ddmd.root.rmem
ddmd.root.rootobject
ddmd.root.stringtable
ddmd.tokens
ddmd.utf

Here we can see that a set of modules is located in the nested package ddmd.root. This package contains common functionality used throughout the DMD source code. Since it doesn’t have any dependencies on any code outside the package it’s a good fit to place in a DUB subpackage. This can be done using the subPackage directive, as follows:

subPackage {
  name "root"
  targetType "library"
  sourcePaths "src/ddmd/root"
}

We specify the name of the subpackage, root. The targetType directive is used to tell DUB whether it should build an executable or a library (though it’s optional — DUB will build an executable if it finds an app.d in the root of the source directory and a library if it doesn’t). Finally, sourcePaths can be used to specify the paths where DUB should look for the D source files if neither of the default directories is used. Fortunately, we want to include all the files in the src/ddmd/root, so using sourcePaths works perfectly fine.

We can verify that the subpackage works and builds by invoking:

$ dub build :root
Building package dmd:root in /Users/jacob/development/d/dlang/dmd/
Performing "debug" build using dmd for x86_64.
dmd:root ~master: building configuration "library"...

:package-name is shorthand that tells DUB to build the package-name subpackage of the current package, in our case the root subpackage.

After removing all the modules from the root package from the initial list of dependencies, the following modules remain:

ddmd.console
ddmd.entity
ddmd.errors
ddmd.globals
ddmd.id
ddmd.identifier
ddmd.lexer
ddmd.tokens
ddmd.utf

The next step is to create a subpackage for the lexer containing the remaning modules.

subPackage {
  name "lexer"
  targetType "library"
  sourcePaths

Again we start by specifying the name of the subpackage and that the target type is a library. Specifying sourcePaths without any value will set it to an empty list, i.e. no source paths. This is done because there are more files than we want to include in this subpackage in the source directory.

sourceFiles \
    "src/ddmd/console.d" \
    "src/ddmd/entity.d" \
    "src/ddmd/errors.d" \
    "src/ddmd/globals.d" \
    "src/ddmd/id.d" \
    "src/ddmd/identifier.d" \
    "src/ddmd/lexer.d" \
    "src/ddmd/tokens.d" \
    "src/ddmd/utf.d"

The above specifies all source files that should be included in this subpackage. The difference between sourcePaths and sourceFiles is that sourcePaths expects a whole directory of source files that should be included, where sourceFiles lists only the individual files that should be included. A list in SDLang is written by separating the items with a space. The backslash (\) is used for line continuation, making it possible spread the list across multiple lines.

The final step of the lexer subpackage is to add a dependency on the root subpackage. This is done with the dependency directive:

dependency "dmd:root" version="*"
}

The first parameter for the dependency directive is the name of another DUB package. The colon is used to separate the package name from the subpackage name. The version attribute is used to specify which version the package should depend on. The * is used to indicate that any version of the dependency matches, i.e. the latest version should always be used. When implementing subpackages in any given package, this is generally what should be used. External projects that depend on any DUB package should specify a SemVer version number corresponding to a known release version.

If we build the lexer subpackage now it will result in an error:

$ dub build :lexer
Building package dmd:lexer in /Users/jacob/development/d/dlang/dmd/
Performing "debug" build using dmd for x86_64.
dmd:lexer ~master: building configuration "library"...
src/ddmd/globals.d(339,21): Error: need -Jpath switch to import text file VERSION
dmd failed with exit code 1.

Looking at the file and line of the error shows that it contains the following code:

_version = (import("VERSION") ~ '\0').ptr;

This code contains an import expression. Import expressions differ from import statements (e.g. import std.stdio;) in that they take a file from the file system and insert its contents into the current module. It’s just as if you copied and pasted the contents yourself. Using an import expression requires that the path where the file is imported from be passed to the compiler as a security mechanism. This can be done using the -J flag. In this case, we want to use the package root, where we are executing DUB, so we can use a single dot: “.“. Passing arbitrary flags to the compiler can be done with the dflags build setting, as follows:

dflags "-J."

Add that to the lexer subpackage configuration and it will compile correctly:

$ dub build :lexer
Building package dmd:lexer in /Users/jacob/development/d/dlang/dmd/
Performing "debug" build using dmd for x86_64.
dmd:lexer ~master: building configuration "library"...

For the final subpackage, we have the parser. The parser is located in src/ddmd/parse.d. To get its dependencies we can use the same approach we used for the lexer. But we will filter out all files that are part of the other subpackages:

$ dmd -deps=deps.txt -Isrc -J. -o- src/ddmd/parse.d
$ cat deps.txt | cut -d ' ' -f 1 | grep ddmd | grep -E -v '(root|console|entity|errors|globals|id|identifier|lexer|tokens|utf)' | sort | uniq
ddmd.parse

Here, we’re supplying the -v flag to grep to filter the results and the -E flag to enable extended regular expressions. All modules from the root package and all modules from the lexer subpackage are filtered out and the only remaining module is the ddmd.parse module.

The subpackage for the parser will look similar to the other subpackages:

subPackage {
  name "parser"
  targetType "library"
  sourcePaths

  sourceFiles "src/ddmd/parse.d"

  dependency "dmd:lexer" version="*"
}

Again, we can verify that it’s working by building the subpackage:

$ dub build :parser
Building package dmd:parser in /Users/jacob/development/d/dlang/dmd/
Performing "debug" build using dmd for x86_64.
dmd:parser ~master: building configuration "library"...

Currently we have three subpackages in the DUB recipe file, but no way to use the main package as a whole. To fix this we add the parser subpackage as a dependency of the main package. We pick the parser subpackage as a dependency because it will include the other two subpackages through its own dependencies.

license "BSL-1.0"

targetType "none"
dependency ":parser" version="*"

subPackage {
  name "root"

In addition to specifying parser as a dependency, we also specify the target type to be none. This will avoid building an empty library out of the main package, since it doesn’t contain any source files of its own.

As a final step, we’ll verify that the whole library is working by creating a separate project that uses the DMD DUB package as a dependency. We create a new DUB project in the test directory, called dub_package:

$ cd test
$ mkdir dub_package
$ cd dub_package
$ cat > dub.sdl <<EOF
> name "dmd-dub-test"
> description "Test of the DMD Dub package"
> license "BSL 1.0"
>
> dependency "dmd" path="../../"
> EOF
$ mkdir source

We create a new file, source/app.d, with the following content:

void main()
{
}

// lexer
unittest
{
    import ddmd.lexer;
    import ddmd.tokens;

    immutable expected = [
        TOKvoid,
        TOKidentifier,
        TOKlparen,
        TOKrparen,
        TOKlcurly,
        TOKrcurly
    ];

    immutable sourceCode = "void test() {} // foobar";
    scope lexer = new Lexer("test", sourceCode.ptr, 0, sourceCode.length, 0, 0);
    lexer.nextToken;

    TOK[] result;

    do
    {
        result ~= lexer.token.value;
    } while (lexer.nextToken != TOKeof);

    assert(result == expected);
}

// parser
unittest
{
    import ddmd.astbase;
    import ddmd.parse;

    scope parser = new Parser!ASTBase(null, null, false);
    assert(parser !is null);
}

The above file contains two unit tests, one for the lexer and one for the parser. We can run dub test to run the unit tests for this package:

$ dub test
No source files found in configuration 'library'. Falling back to "dub -b unittest".
Performing "unittest" build using dmd for x86_64.
dmd:root ~issue-17392-dub: building configuration "library"...
dmd:lexer ~issue-17392-dub: building configuration "library"...
../../src/ddmd/globals.d(339,21): Error: file "VERSION" cannot be found or not in a path specified with -J
dmd failed with exit code 1.

Which gives us the error that it cannot find the VERSION file in any string import paths, even though we added the correct directory to the string import paths. If we run the tests with verbose output enabled, using the --verbose flag we get a hint (the output has been reduced to save space):

dmd:lexer ~issue-17392-dub: building configuration "library"...
dmd -J. -lib

Here we see that the compiler is invoked with the -J. flag, which is what we previously specified in the lexer subpackage. The problem is that the current directory is now of the dmd-dub-test DUB package instead of the dmd DUB package. Looking at the documentation of DUB we can see there’s an environment variable, $PACKAGE_DIR, that we can use as the string import path instead of hardcoding it to use a single dot. We update the dflags setting of the lexer subpackage to use the $PACKAGE_DIR environment variable:

dflags "-J$PACKAGE_DIR"
}

Running the tests again shows that the error is fixed, but now we get a new error, a long list of undefined symbols (shortened here):

$ dub test
No source files found in configuration 'library'. Falling back to "dub -b unittest".
Performing "unittest" build using dmd for x86_64.
dmd:root ~issue-17392-dub: building configuration "library"...
dmd:lexer ~issue-17392-dub: building configuration "library"...
dmd:parser ~issue-17392-dub: building configuration "library"...
dmd-dub-test ~master: building configuration "application"...
Linking...
Undefined symbols for architecture x86_64:
  "_D4ddmd7astbase12__ModuleInfoZ", referenced from:
      _D3app12__ModuleInfoZ in dmd-dub-test.o

The reason for this is that we’re importing the ddmd.astbase module in the test of the parser, but it’s never compiled. We can solve that problem by adding it to the parser subpackage in the dmd DUB package. Running dmd again to show all its dependencies shows that it also depends on the ddmd.astbasevisitor module. We add these two modules as follows:

sourceFiles \
  "src/ddmd/astbase.d" \
  "src/ddmd/astbasevisitor.d" \
  "src/ddmd/parse.d"

Finally, running the tests again shows that everything is working correctly:

$ dub test
No source files found in configuration 'library'. Falling back to "dub -b unittest".
Performing "unittest" build using dmd for x86_64.
dmd:root ~issue-17392-dub: building configuration "library"...
dmd:lexer ~issue-17392-dub: building configuration "library"...
dmd:parser ~issue-17392-dub: building configuration "library"...
dmd-dub-test ~master: building configuration "application"...
Linking...
Running ./dmd-dub-test

After verifying that both the lexer and parser are working in a separate DUB package, this is the final result of the package recipe for the dmd DUB package:

name "dmd"
description "The DMD compiler"
authors "Walter Bright"
copyright "Copyright © 1999-2017, Digital Mars"
license "BSL-1.0"

targetType "none"
dependency ":parser" version="*"

subPackage {
  name "root"
  targetType "library"
  sourcePaths "src/ddmd/root"
}

subPackage {
  name "lexer"
  targetType "library"
  sourcePaths

  sourceFiles \
    "src/ddmd/console.d" \
    "src/ddmd/entity.d" \
    "src/ddmd/errors.d" \
    "src/ddmd/globals.d" \
    "src/ddmd/id.d" \
    "src/ddmd/identifier.d" \
    "src/ddmd/lexer.d" \
    "src/ddmd/tokens.d" \
    "src/ddmd/utf.d"

  dflags "-J$PACKAGE_DIR"

  dependency "dmd:root" version="*"
}

subPackage {
  name "parser"
  targetType "library"
  sourcePaths

  sourceFiles \
    "src/ddmd/astbase.d" \
    "src/ddmd/astbasevisitor.d" \
    "src/ddmd/parse.d"

  dependency "dmd:lexer" version="*"
}

All this has now been merged into master and the DUB package is available here: http://code.dlang.org/packages/dmd. Happy hacking!

Project Highlight: Funkwerk

Funkwerk is a German company that develops intelligent communication technology. One of their projects is a passenger information system for long-distance and local transport that is deployed by long-distance rail networks in Germany, Austria, Switzerland, Finland, Norway and Luxembourg, as well as city railways in Berlin and Munich. The system is developed at the company’s Munich location and, some time ago, they came to the conclusion that it needed a rewrite. According to Funkwerk’s Mario Kröplin:

From a bird’s-eye view, the long-term replacement of our aged passenger information system is our primary project. At some point, it became obvious that the system was getting hard to maintain and hard to change. In 2008, a new head of the development department was hired. It was time for a change.

They wanted to do more than just rewrite the system. They also wanted to make changes in their development process, and that led to questions of how best to approach the changes so that they didn’t negatively impact productivity.

There is an inertia when experienced programmers are asked to suddenly start with unit testing and code reviews. The motivation for unit testing and code reviews is higher when you learn a new programming language. Especially one where unit testing is a built-in feature. It takes a new language to break old habits.

The decision was made to select a different language for the project, preferably one with built-in support for unit testing. They also allowed themselves a great deal of leeway in making that choice.

Compared with other monolithic legacy systems, we were in the advantageous position that the passenger information system was constructed as a set of services. There is some interprocess communication between the services, but otherwise the services are largely independent. The service architecture made such a decision less serious: just make an experiment with any one of the services in the new language. Should the experiment fail, then it’s not a heavy loss.

So they cast out a net and found D. One thing about the language that struck them immediately was that it fit in a comfort zone somewhere between the languages with which their team was already familiar.

The languages in use were C++ (which rather feels like being part of the problem than part of the solution) and Java. D was presented as a better C++. With garbage collection, changing to D is easy enough for both C++ and Java programmers.

They took D for a spin with some experimentation. The first experiment was a bit of a failure, but the second was a success. In fact, it went well enough that it convinced them to move forward with D.

In retrospect, the change of programming language was one aspect that led to an agile transition. One early example of a primary D project was the replacement of the announcement service. This one gets all information about train journeys – especially about the disruptions – and decides when to play which announcements. The business rules make this quite complicated. The new service uses object-oriented programming to handle the complicated business rules nicely. So you could say it’s a Java program written in D. However, as the business rules change from customer to customer, the announcement service is constantly changing. And with the development of D and with our learning, we are now in a better position to evolve the software further.

When they first started the changeover, D2 was still in its early stages of development and the infamous Phobos/Tango divide was still a thing in the D community (an issue that, for those of you out of the loop the last several years, was put to rest long ago). Since D1 was considered stable, that was an obvious choice for them. They also chose Tango over Phobos, primarily because Tango provided logging and HTTP facilities, while Phobos did not. In 2012, as D2 was stablizing and official support for D1 was being phased out, it became apparent that they would have to make a transition from D1 and Tango to D2 and Phobos, which is what they use today.

One of the D features that hooked Mario early on was its built-in support for contracts.

Contracts won me over from the beginning. No joke! When we had hard times to hunt down segmentation faults, a failed assertion boosted the diagnostics. I used the assert macro a lot in C++, but with contracts in D there is no question about whom to blame. A failed precondition: caller’s fault. A failed postcondition or invariant: my fault. We have contracts everywhere. And we don’t use the -release switch. We would rather give up performance than information that helps us fixing bugs.

As with many other active D users, D’s dynamic arrays, slices and the built-in associative arrays stand out for the Funkwerk programmers

They work out of the box. It’s not like in Java, where you’d better not use the arrays of the language, but the ArrayList of the library.

They also have put D’s ranges and Uniform Function Call Syntax to heavy use.

With ranges and UFCS, it became possible to replace loops with intention-revealing chains of map and filter and whatever. While the loop describes how something is done, the ranges describe what is done. We enjoy setting the delay in our tests to 5.minutes. And we use UDAs in accessors and dunit. It’s hard to imagine what pieces of our clean code would look like in any other language.

accessors is a library that uses templates, mixins, and UDAs to automatically generate property getters and setters. dunit is a unit testing library that, in an ironic twist, Mario maintains. As it turned out, their intuition that their developers would be more motivated to write code when a language has built-in unit testing was correct (that’s been cited by Walter Bright as a reason it was included in the language in the first place). However, that first failed experiment in D came down to the simplistic capabilities the built-in unit testing provides.

One of the failures of our first experiment was an inappropriate use of the unittest functions. You’re on your own when you have to write more code per test case, for example for testing interactions of objects. With xUnit testing frameworks like JUnit, you get a toolbox and lots of instructions, like the exhaustive http://xunitpatterns.com/. With D, you get the single tool unittest and lots of examples of test cases that can be expressed as one-liners. This does not help in scenarios where such test cases are the exception. Even Phobos has examples where the unittest blocks are lengthy and obscure. Often, such unittest blocks are not clean code but a collection of “Test Smells”. We soon replaced the built-in unit testing (one of the benefits we’ve seen) with DUnit.

The transition to D2 and Phobos necessitated a move away from the Tango-based DUnit. Mario found the open source (and lowercase) dunit (the original project is here) a promising replacement, and ultimately found himself maintaining a fork that has evolved considerably from the original. However, the team recently discovered a way to combine xUnit testing with D’s built-in unittest, which may lead to another transition in their unit testing.

Additionally, Phobos still had no logging package in 2012, so they needed a replacement for the Tango logger API. Mario rolled up his sleeves and implemented log, which, like acessors and dunit, is available under the Boost Software License.

It was meant as an improvement proposal for an early stage of  std.experimental.logger. I was excited that it was possible to implement the logging framework together with support for rolling log files, for logrotate, and for syslog in just 500 lines. We’re still using it.

Funkwerk has also contributed code to Phobos.

I was looking for a way to implement a delay calculation. The problem is that you have an initial delay and you assume that the driver runs faster and makes shorter stops in order to catch up. But the algorithm was missing. So we made a pull request for std.algorithm.cumulativeFold. It’s there because we strived for clean code in a forecast service.

Garbage collection has been a topic of interest on this blog lately. Mario has an interesting anecdote from Funkwerk’s usage of D’s GC.

Our service was losing connections from time to time. It took a while to find that the cause was the interaction between garbage collection and networking. The signals used by the garbage collector interrupt the blocking read. The blocking read is hidden in a library, for example, in std.socketstream, where the retry is missing. Currently, we abuse inheritance to patch the SocketStream class.

The technical description of the derived class from the Ddoc comments read:

/**
 * When a timeout has been set on the socket, the interface functions "recv"
 * and "send" are not restarted after being interrupted by a signal handler.
 * They fail with the error "EINTR" when interrupted, especially
 * by the signal used to "stop the world" during garbage collection.
 *
 * In this case, retries avoid that the stream is mistaken to be exhausted.
 * Note, however, that these retries will likely exceed the specified timeout.
 *
 * See also: Linux manual page signal(7)
 */

After nearly a decade of active development with D, Mario and his team have been pleased with their choice. They plan to continue using the language in new projects.

Over the years, we’ve replaced services in the periphery of the passenger information system whenever this was justified by customer needs. We chose D for the implementation of all of the backend services. We are now working on a small greenfield project (it’s a few buses to be monitored instead of lots of trains), but we intend to grow this project into the next generation of the passenger information system.

In the coming months, we’ll hear more from Mario and the Funkwerk developers about how they use D to develop their system and tooling, and what it’s like to be a professional D programmer.

New D Compiler Release: DMD 2.075.0

DMD 2.075.0 was released a few days back. As with every release, the changelog is available so you can browse the list of fixed bugs and new features. 2.075.0 can be fetched from the dlang.org download page, which always makes available the latest DMD release alongside a nightly build.

Notable Changes

Every DMD release brings with it a number of bug fixes, changes, and enhancements. Here are some of the more noteworthy changes in this release.

Two array properties removed

Anyone who does a lot of work with D’s ranges will likely have encountered this little annoyance that arises from the built-in .sort property of arrays.

void main()
{
    import std.algorithm : remove, sort;
    import std.array : array;
    int[] nums = [5, 3, 1, 2, 4];
    nums = nums.sort.remove(2).array;
}

The .sort property has been deprecated for ages, so the above would result in the following error:

sorted.d(6): Deprecation: use std.algorithm.sort instead of .sort property

The workaround would be to add an empty set of parentheses to the sort call. With DMD 2.075.0, this is no longer necessary and the above will compile. Both the .sort and .reverse array properties have finally been removed from the language.

For the uninitiated, D has two features that have proven convenient in the functional pipeline programming style typically used with ranges. One is that parentheses on a function call are optional when there are no parameters. The other is Universal Function Call Syntax (UFCS), which allows a function call to be made using the dot notation on the first argument, so that a function int add(int a, int b) can be called as: 10.add(5).

Each of D’s built-in types comes with a set of built-in properties. Given that the built-in properties are not functions, no parentheses are used to access them. The .sort array property has been around since the early days of D1. At the time, it was rather useful and convenient for anyone who was happy with the default implementation. When D2 came along with the range paradigm, the standard library was given a set of functions that can treat arrays as ranges, opening them up to use with the many range based functions in the std.algorithm package and elsewhere.

With optional parentheses, UFCS, and a range-based function in std.algorithm called sort, conflict was inevitable. Now range-based programmers can put that behind them and take one more pair of parentheses out of their pipelines.

The breaking up of std.datetime

The std.datetime module has had a reputation as the largest module in D’s standard library. Some developers have been known to use it a stress test for their tooling. It was added to the library long before D got the special package module feature, which allows multiple modules in a package to be imported as a single module.

Once package modules were added, Jonathan M. Davis, the original std.datetime developer, found it challenging to split the monolith into multiple modules. Then, at DConf 2017, he could be seen toiling away on his laptop in the conference hall and the hotel lobby. On the final day of the conference, the day of the DConf Hackathon, he announced that std.datetime was now a package. DMD 2.075.0 is the first release where the new module structure is available.

Any existing code using the old module should still compile. However, any static libraries or object files lying around with the old symbols stuffed inside may need to be recompiled.

Colorized compiler messages

This one is missing from the changelog. DMD now has the ability to output colorized messages. The implementation required going through the existing error messages and properly annotating them where appropriate, so there may well be some messages for which the colors are missing. Also, given that this is a brand new feature and people can be picky about their terminal colors, more work will likely be done on this in the future. Perhaps that might include support for customization.

 

Compiler Ddoc documentation online

DMD, though originally written in C++, was converted to D some time ago. Now that more D programmers are able to contribute to the compiler, work has gone into documenting its source using D’s built-in Ddoc syntax. The result is now online, accessible from the sidebar of the existing library reference. A good starting point is the ddmd.mars module.

And more…

The above is a small part of the bigger picture. The bugfix list shows 89 bugs, regressions, and enhancements across the compiler, runtime, standard library, and web site. See the full changelog for the details.

Thanks to everyone who contributed to this release, whether it was by reporting issues, submitting or reviewing pull requests, testing out the beta, or carrying out any of the numerous small tasks that help a new release see the light of day.

DCompute: GPGPU with Native D for OpenCL and CUDA

Nicholas Wilson is a student at Murdoch University, studying for his BEng (Hons)/BSc in Industrial Computer Systems (Hons) and Instrumentation & Control/ Molecular Biology & Genetics and Biomedical Science. He just finished his thesis on low-cost defect detection of solar cells by electroluminescence imaging, which gives him time to work on DCompute and write about it for the D Blog. He plays the piano, ice skates, and has spent 7 years putting D to use on number bashing, automation, and anything else that he could make a computer do for him.

DCompute is a framework and compiler extension to support writing native kernels for OpenCL and CUDA in D to utilise GPUs and other accelerators for computationally intensive code. In development are drivers to automate the interactions between user code and the tedious and error prone compute APIs with the goal of enabling the rapid development of high performance D libraries and applications.

Introduction

After watching John Colvin’s DConf 2016 presentation in May of last year on using D’s metaprogramming to make the OpenCL API marginally less horrible to use, I thought, “This would be so much easier to do if we were able to write kernels in D, rather than doing string manipulations in OpenCL C”. At the time, I was coming up to the end of a rather busy semester and thought that would make a good winter[1] project. After all, LDC, the LLVM D Compiler, has access to LLVM’s SPIR-V and PTX backends, and I thought, “It can’t be too hard, its only glue code”. I slightly underestimated the time it would take, finishing the first stage of DCompute (because naming things is hard), mainlining the changes I made to LDC at the end of February, eight months later — just in time for the close of submissions to DConf, where I gave a talk on the progress I had made.

Apart from familiarising myself with the LDC and DMD front-end codebases, I also had to understand the LLVM SPIR-V and PTX backends that I was trying to target, because they require the use of special metadata (for e.g. denoting a function is a kernel) and address spaces, used to represent __global & friends in OpenCL C and __global__ & friends in CUDA, and introduce these concepts into LDC.

But once I was familiar with the code and had sorted the above discrepancies, it was mostly smooth sailing translating the OpenCL and CUDA modifiers into compiler-recognised attributes and wrapping the intrinsics into an easy to use and consistent interface.

When it was all working and almost ready to merge into mainline LDC, I hit a bit of a snag with regards to CI: the SPIR-V backend that was being developed by Khronos was based on the quite old LLVM 3.6.1 and, despite my suggestions, did not have any releases. So I forward ported the backend and the conversion utility to the master branch of LLVM and made a release myself. Still in progress on this front are converting magic intrinsics to proper LLVM intrinsics and transitioning to a TableGen-driven approach for the backend in preparation for merging the backend into LLVM Trunk. This should hopefully be done soon™.

Current state of DCompute

With the current state of DCompute we are able to write kernels natively in D and have access to most of its language-defining features like templates & static introspection, UFCS, scope guards, ranges & algorithms and CTFE. Notably missing, for hardware and performance reasons, are those features commonly excluded in kernel languages, like function pointers, virtual functions, dynamic recursion, RTTI, exceptions and the use of the garbage collector. Note that unlike OpenCL C++ we allow kernel functions to be templated and have overloads and default values. Still in development is support for images and pipes.

Example code

To write kernels in D, we need to pass -mdcompute-targets=<targets> to LDC, where <targets> is a comma-separated list of the desired targets to build for, e.g. ocl-120,cuda-350 for OpenCL 1.2 and CUDA compute capability 3.5, respectively (yes, we can do them all at once!). We get one file for each target, e.g. kernels_ocl120_64.spv, when built in 64-bit mode, which contains all of the code for that device.

The vector add kernel in D is:

@compute(CompileFor.deviceOnly) module example;
import ldc.dcompute;
import dcompute.std.index;

alias gf = GlobalPointer!float;

@kernel void vadd(gf a, gf b, gf c) 
{
	auto x = GlobalIndex.x;
	a[x] = b[x]+c[x];
}

Modules marked with the @compute attribute are compiled for each of the command line targets, @kernel makes a function a kernel, and GlobalPointer is the equivalent of the __global qualifier in OpenCL.

Kernels are not restricted to just functions — lambdas & tamplates also work:

@kernel void map(alias F)(KernelArgs!F args)
{
    F(args);
}
//In host code
AutoBuffer!float x,y,z; // y & z initialised with data
q.enqueue!(map!((a,b,c) => a=b+c))(x.length)(x, y, z);

Where KernelArgs translates host types to device types (e.g. buffers to pointers or, as in this example, AutoBuffers to AutoIndexed Pointers) so that we encapsulate the differences in the host and device types.

The last line is the expected syntax for launching kernels, q.enqueue!kernel(dimensions)(args), akin to CUDA’s kernel<<<dimensions,queue>>>(args). The libraries for launching kernels are in development.

Unlike CUDA, where all the magic for transforming the above expression into code on the host lies in the compiler, q.enqueue!func(sizes)(args) will be processed by static introspection of the driver library of DCompute.
The sole reason we can do this in D is that we are able to query the mangled name the compiler will give to a symbol via the symbol’s .mangleof property. This, in combination with D’s easy to use and powerful templates, means we can significantly reduce the mental overhead associated with using the compute APIs. Also, implementing this in the library will be much simpler, and therefore faster to implement, than putting the same behaviour in the compiler. While this may not seem much for CUDA users, this will be a breath of fresh air to OpenCL users (just look at the OpenCL vector add host code example steps 7-11).

While you cant do that just yet in DCompute, development should start to progress quickly and hopefully become a reality soon.

I would like to thank John Colvin for the initial inspiration, Mike Parker for editing, and the LDC folks, David Nadlinger, Kai Nacke, Martin Kinke, with a special thanks to Johan Engelen, for their help with understanding the LDC codebase and reviewing my work.

If you would like to help develop DCompute (or be kept in the loop), feel free to drop a line at the libmir Gitter. Similarly, any efforts preparing the SPIR-V backend for inclusion into LLVM are also greatly appreciated.

[1] Southern hemisphere.

Go Your Own Way (Part One: The Stack)

This is my third post in the GC series. In the first post, I introduced D’s garbage collector and the language features that require it, and touched on simple strategies to use it effectively. In the second post, I showed off the tools provided by the language and library to disable or prohibit the GC in specific parts of a code base, how to use the compiler to assist in that endeavor, and recommended that D programs be written initially to embrace the GC, taking advantage of simple strategies to mitigate its impact, and later tuned to avoid it or further optimize its usage only when profiling shows it’s warranted.

When garbage collection is turned off via GC.disable or prevented by the @nogc function annotation, memory will still need to be allocated from somewhere. And even when the GC is fully embraced, it’s still desirable to minimize the size and quantity of GC heap allocations. That means allocating either via the stack, or via the non-GC heap. The focus of this post is the former. Non-GC heap allocations will be covered in my next post in this series.

Stack allocation

The simplest allocation strategy in D is the same as it is in C: avoid the heap and use the stack whenever possible. When a local array is needed and the size can be known at compile time, use a static rather than a dynamic array. Structs, which are value types and stack-allocated by default, should be preferred where possible over classes, which are reference types and are usually allocated from one heap or another. D’s compile-time features can present opportunities here that might not otherwise be available.

Static arrays

Static array declarations in D require the length to be known at compile-time.

// OK
int[10] nums;

// Error: variable x cannot be read at compile time
int x = 10;
int[x] err;

Unlike dynamic arrays, static arrays can be initialized with array literals with no allocation taking place on the GC heap. The lengths must match, otherwise the compiler will emit an error.

@nogc void main() {
    int[3] nums = [1, 2, 3];
}

Static arrays are automatically sliced when passed to any function taking a slice as a parameter, making them interchangeable with dynamic arrays.

void printNums(int[] nums) {
    import std.stdio : writeln;
    writeln(nums);
}

void main() {
    int[]  dnums = [0, 1, 2];
    int[3] snums = [0, 1, 2];
    printNums(dnums);
    printNums(snums);
}

When compiling with -vgc to find the potential GC allocations in a program and eliminate them where possible, this is an easy win. Just be wary of situations like the following:

int[] foo() {
    auto nums = [0, 1, 2];

    // Do work with nums...

    return nums;
}

Converting nums in this example to a static array would be a mistake. The return statement in that case would be returning a slice to stack-allocated memory, which is a programming error. Luckily, doing so will generate a compiler error.

On the other hand, if the return is conditional, it may be desirable to heap-allocate the array only when absolutely necessary rather than every time the function is called. In that scenario, a static array can be declared locally and a dynamic copy made on return. Enter the .dup property:

int[] foo() {
    int[3] nums = [0, 1, 2];
    
    // Let x = the result of some work with nums
    bool condtion = x;

    if(condition) return nums.dup;
    else return [];
}

This function still uses the GC via .dup, but only allocates if it needs to and avoids allocation when it doesn’t. Note that [] is equivalent to null in this case, a slice (or dynamic array) with a .length of 0 and a .ptr of null.

Structs vs. classes

Struct instances in D are allocated on the stack by default, but can be allocated on the heap when desired. Stack-allocated structs have deterministic destruction, with their destructors called as soon as the enclosing scope exits.

struct Foo {
    int x;
    ~this() {
        import std.stdio;
        writefln("#%s says bye!", x);
    }
}
void main() {
    Foo f1 = Foo(1);
    Foo f2 = Foo(2);
    Foo f3 = Foo(3);
}

As expected, this prints:

#3 says bye!
#2 says bye!
#1 says bye!

Classes, being reference types, are almost always allocated on the heap. Usually, that’s the GC heap via new, though it could also be the non-GC heap through a custom allocator. But there’s no rule saying they can’t be allocated on the stack. The standard library template std.typecons.scoped allows us to easily do so.

class Foo {
    int x;

    this(int x) { 
        this.x = x; 
    }
    
    ~this() {
        import std.stdio;
        writefln("#%s says bye!", x);
    }
}
void main() {
    import std.typecons : scoped;
    auto f1 = scoped!Foo(1);
    auto f2 = scoped!Foo(2);
    auto f3 = scoped!Foo(3);
}

Functionally, this is identical to the struct example above; it prints the same results. Deterministic destruction is achieved via the core.object.destroy function, which allows destructors to be called outside of GC collections.

Note that neither scoped nor destroy are currently usable in @nogc functions. This isn’t necessarily a problem, as a function doesn’t have to be annotated such to avoid the GC, but it can be a headache if you are trying to fit everything into a @nogc call tree. In future posts, we’ll look at some of the design issues that crop up when using @nogc and how to avoid them.

Generally, when implementing custom types in D, the choice between struct and class should be dependent on the type’s intended usage. POD types are obvious candidates for struct, whereas for types in something like a GUI system, where inheritance heirarchies and runtime interfaces are extremely useful, class is a more appropriate choice. Beyond those obvious cases, there are a number of other considerations which could be the focus of a separate blog post on the topic. For our purposes, just keep in mind that whether or not a type is implemented as a struct or a class need not always dictate whether or not instances can be allocated on the stack.

alloca

Given that D makes alloca available, it is also an option for stack allocation. This is a candidate especially for arrays when you want to avoid or eliminate a local GC allocation, but the array size is only known at run time. The following example allocates a dynamic array with a runtime size on the stack.

import core.stdc.stdlib : alloca;

void main() {
    size_t size = 10;
    void* mem = alloca(size);

    // Slice the memory block
    int[] arr = cast(int[])mem[0 .. size];
}

The same caution about using alloca in C applies here: be careful not to blow up the stack. And as with local static arrays, don’t return a slice of arr. Return arr.dup instead.

A simple example

Consider an implementation of a Queue data type. An idiomatic implementation in D is going to be a struct that’s templated on the type it’s intended to contain. In Java, collection usage is interface heavy and it’s recommended to declare an instance using an interface type rather than the implementation type. Structs in D can’t implement interfaces, but in many cases they can still be used to program to interfaces thanks to Design by Introspection (DbI). This allows programming to a common interface that is verified via compile-time introspection without the need for an interface type, so it can work with structs, classes and, thanks to Universal Function Call Syntax (UFCS), even free functions (when the functions are in scope).

D’s arrays are an obvious choice as the backing store for a Queue implementation. Moreover, there’s an opportunity to make the backing store a static array when a queue is intended to be bounded with a fixed size. Since it’s already a templated type, an additional parameter, a template value parameter with a default value can easily be added to decide at compile time if the array should be static or not and, if so, how much space it should require.

// A default Size of 0 means to use a dynamic array for the
// backing store; non-zero indicates a static array.
struct Queue(T, size_t Size = 0) 
{
    // This constant will be inferred as a boolean. By making it
    // public, a DbI template outside of this module can determine
    // whether or not the Queue might grow. 
    enum isFixedSize = Size > 0;

    void enqueue(T item) 
    {
        static if(isFixedSize) {
            assert(_itemCount < _items.length);
        }
        else {
            ensureCapacity();
        }
        push(item);
    }

    T dequeue() {
        assert(_itemCount != 0);
        static if(isFixedSize) {
            return pop();
        }
        else {
            auto ret = pop();
            ensurePacked();
            return ret;
        }
    }

    // Only available on a growable array
    static if(!isFixedSize) {
        void reserve(size_t capacity) { /* Allocate space for new items */ }
    }

private:   
    static if(isFixedSize) {
        T[Size] _items;     
    }
    else T[] _items;
    size_t _head, _tail;
    size_t _itemCount;

    void push(T item) { 
        /* Add item, update _head and _tail */
        static if(isFixedSize) { ... }
        else { ... }
    }

    T pop() { 
        /* Remove item, update _head and _tail */ 
        static if(isFixedSize) { ... }
        else { ... }
    }

    // These are only available on a growable array
    static if(!isFixedSize) {
        void ensureCapacity() { /* Alloc memory if needed */ }
        void ensurePacked() { /* Shrink the array if needed */}
    }
}

With this, the client can declare instances like so:

Queue!Foo qUnbounded;
Queue!(Foo, 128) qBounded;

qBounded requires no heap allocations. What happens with qUnbounded depends on the implementation. Moreover, compile-time introspection can be used to test if an instance is a fixed size or not. The isFixedSize constant is a convenience for that. Clients could alternatively use the built-in __traits(hasMember, T, "reserve") or the standard library function std.traits.hasMember!T("reserve") in one compile-time construct or another (__traits and std.traits are great for DbI, and the latter should be preferred when it provides similar functionality), but including the constant in the type is more convenient.

void doSomethingWithQueueInterface(T)(T queue)
{
    static if(T.isFixedSize) { ... }
    else { ... }
}

Conclusion

This has been a brief overview of a few options for stack allocation in D to avoid allocations from the GC heap. Making use of them when possible is an easy way to minimize the size and quantity of GC allocations, a proactive strategy for mitigating potential negative performance impacts from garbage collection.

The next post in this series will cover some of the options available for non-GC heap allocations.

Project Highlight: Derelict

Previous project highlights on this blog were written up both in my own words and in quotes from the project maintainers. This time around is different — it would be a little odd to quote myself while writing about my own project.

Derelict is a collection of D bindings to C libraries. In its present incarnation, it resides in a GitHub organization called DerelictOrg. There you’ll find bindings to a number of libraries such as SDL, GLFW, SFML, OpenGL, OpenAL, FreeType, FreeImage and more. There are currently 26 repositories in the organization: one for the documentation, one for a utility package, 21 active packages and three that are currently unsupported, maintained primarily by me and Guillaume Piolat.

The Derelict packages primarily provide dynamic bindings, though some can be configured as static bindings at compile time. Dynamic bindings require the C libraries to which they bind to be dynamically loaded at run time. The mechanism for this is provided by the DerelictUtil package. Loading a library is as simple as a function call, e.g. DerelictSDL2.load();.

Static bindings have a link-time dependency, requiring either statically or dynamically linking with the bound C library when building an executable. In that case, the dependency on DerelictUtil goes away (some packages may still use a few DerelictUtil declarations, requiring one of its modules to be imported, but it need not be linked into the program). On Windows, this introduces potential issues with object file formats, but these days they are quite easy to solve.

It’s hard to believe now, but Derelict has been around continuously, in one form or another, since March of 2004. I was first drawn to D because it fit almost perfectly between the two languages I had the most experience with back in 2003, C and Java. It addressed some of the frustrations I had with each while combining things I loved from both. But early on I ran into my first frustration with D.

At the time, interfacing with C libraries on Windows was a bit annoying. D has excellent support for and compatibility with C libraries in the language, but the object file format issues I alluded to above were frustrating. DMD on Windows could only output object files in the OMF format and could only use the OPTLINK linker, an ancient 32-bit linker that only understands OMF. This is because DMD was implemented on top of backend and tool chain used by the DMC, the Digital Mars C and C++ compiler. Meanwhile, the rest of the Microsoft ecosystem was (and is) using the COFF format. In practice, a static binding to a C library required either using tools to convert the object files or libraries from OMF to COFF, or compiling the C library with DMC. You don’t tend to find support for DMC in most build scripts.

When I found that the bindings for the libraries I wanted to use (SDL and OpenGL) were static, I resolved to create my own. I’m a big fan of dynamic loading, so it was a no-brainer to create dynamic bindings. When you load a DLL via the LoadLibrary/GetProcAddress API, the object file format is irrelevant. From that point on, I never had to worry about the COFF/OMF problem again. Even though the problem didn’t exist outside of Windows, I made them cross platform anyway to keep a consistent interface.

In those days, DMD 1.0.0 wasn’t yet a thing, but a new web site, dsource.org, had just been launched to host open source D projects (much of the site is still online in archive mode thanks to Vladimir Panteleev). All of the projects there used Subversion, so I decided to learn my way around it and take a stab at maintaining an open source project by making my new bindings available. Part of my motivation for picking “Derelict” as the title is because I fully expected no one else would be interested and, whether they were or not, I would eventually abandon it anyway.

As it turned out, people really were interested. Contributions started coming in almost immediately, along with questions and suggestions on the DSource forums. I added new bindings, set up criteria on new binding submissions (they must be gamedev-related and cross-platform), and added documentation on how to create “Derelictified” bindings. Tomasz Stachowiak (now a Frostbite game engine developer) came along and started making contributions, including a templated loader in DerelictUtil that replaced the one I had hacked together and became the basis for Derelict2.

Eventually, with the dawning of the D2 era, D development moved away from DSource to GitHub. So did Derelict, in the guise of Derelict 3. I had made use of every D build tool that came along before then, but finally settled on a custom build script (written in D) for this iteration, since the build tools all died. When DUB came along and looked like it was here to stay, I fully committed to it. I took the opportunity to finally split up the monolithic repository, gave every package its own repo, and created DerelictOrg as their new home. Aside from the appalling lack of documentation (a state which is rapidly changing, but is still a WIP), things have been fairly stable.

If I were still counting from Derelict 3, I think we might be at Derelict 6 by now, but that’s not how it rolls anymore. Since the move to DerelictOrg, I’ve twice made iterative improvements to DerelictUtil that broke binary compatibility with previous releases. The first time around, I didn’t properly manage the roll out. Anyone building the Derelict libraries manually, i.e. when they weren’t using DUB to manage their application project, could run into issues where one package used the new version and another used the older. It seems like it ought to have been a mess, but I heard very little about it. Still, in the most recent overhaul, I took steps to keep the new stuff distinctly separate from the old stuff and rolled it all out at once (which is why most of the latest releases as I write have a -beta suffix).

As D has evolved over the years, so has Derelict. Now that DMD supports COFF on Windows, some of the packages can now be configured at compile time as a static binding. Long ago, DerelictUtil gained the ability to selectively ignore missing symbols (curiously missing from the latest docs, but described in the old DerelictUtil Wiki) and more recently gained a feature that enables a package implementation to support multiple versions of a C library via a configurable call to the loader (SharedLibVersion), which only a handful of packages support and few more likely will (due to API compatibility issues with type sizes or enum values, not every package can). The latest version of DerelictGL3 is massively configurable at compile time. For a little while, I even relaxed my criteria for allowing new packages under the DerelictOrg umbrella, but now after I ended up being wholly responsible for them that’s something I’m not inclined to continue. With the DUB registry, it’s not really necessary.

There are a number of C library bindings using DerelictUtil out in the wild. You can find them, as well as all of the DerelictOrg packages, at the DUB package registry. Some of the third-party bindings have a name like “derelict-extras-foo”, but others use “derelict-foo” like those in DerelictOrg. You can also find a collection of static bindings in the Deimos organization, and third-party static bindings in the DUB registry that have adopted the Deimos approach of providing C headers along side the D modules.

Anyone who needs help with any of the Derelict packages can often find it in the #D irc channel at freenode.net. I drop by infrequently, but I monitor the D forums every day. Asking for Derelict help in the Learn forum is not off-topic.

The Derelict bindings have gone well beyond targeting game developers. Though they’re often used in games (like Voxelman and the first D game on Steam, Mayhem Intergalactic), they are also used elsewhere (like DLangUI). If you’re using any of the Derelict bindings in your own projects, I’d love to hear about it. Particularly since I’m always looking for projects I’ve never heard about to highlight here on the blog.