I like some features of D, but would be interested if they come with a runtime penalty?

To compare, I implemented a simple program that computes scalar products of many short vectors both in C++ and in D. The result is surprising:

• D: 18.9 s [see below for final runtime]
• C++: 3.8 s

Is C++ really almost five times as fast or did I make a mistake in the D program (it is my first one, please forgive me)?

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Summary of answers: D is as fast as C++ (for numerical computations) if

1. one uses the correct optimization options (-O -release -inline -m64)
2. uses gdc/gdmd instead of dmd
3. uses correct D calling conventions (in vector_t x instead of const ref vector_t x in the arguments to function scalar_product below; --- while replacing the for loops by foreach looks nicer, but does not affect runtimes).

Here all the figures on my machine:

• D (const ref arguments, dmd compiler, correct optimization): 9.6s
• D (in arguments, dmd compiler, correct optimization): 6.1s
• D (in arguments, gdmd compiler, correct optimization): 3.9s
• C++: 3.8s

I used gdc/gdmd 4.3. The combination D(const ref arguments, gdmd compiler) did not compile and thus has not been tested. Aside: gdc/gdmd did not have the datetime library yet and compiling it from sources failed for me, so it seems not as easy to handle as dmd right now.

Thanks CyberShadow and GMan and all others!

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Continuation of original question:

I compiled C++ with g++ -O3 (gcc-snapshot 2011-02-19) and D with dmd -O (dmd 2.052) on a moderate recent linux desktop. The results are reproducible over several runs and standard deviations negligible.

Here the C++ program:

#include <iostream>
#include <random>
#include <chrono>
#include <string>

#include <vector>
#include <array>

typedef std::chrono::duration<long, std::ratio<1, 1000>> millisecs;
template <typename _T>
long time_since(std::chrono::time_point<_T>& time) {
      long tm = std::chrono::duration_cast<millisecs>( std::chrono::system_clock::now() - time).count();
  time = std::chrono::system_clock::now();
  return tm;
}

const long N = 20000;
const int size = 10;

typedef int value_type;
typedef long long result_type;
typedef std::vector<value_type> vector_t;
typedef typename vector_t::size_type size_type;

inline value_type scalar_product(const vector_t& x, const vector_t& y) {
  value_type res = 0;
  size_type siz = x.size();
  for (size_type i = 0; i < siz; ++i)
    res += x[i] * y[i];
  return res;
}

int main() {
  auto tm_before = std::chrono::system_clock::now();

  // 1. allocate and fill randomly many short vectors
  vector_t* xs = new vector_t [N];
  for (int i = 0; i < N; ++i) {
    xs[i] = vector_t(size);
      }
  std::cerr << "allocation: " << time_since(tm_before) << " ms" << std::endl;

  std::mt19937 rnd_engine;
  std::uniform_int_distribution<value_type> runif_gen(-1000, 1000);
  for (int i = 0; i < N; ++i)
    for (int j = 0; j < size; ++j)
      xs[i][j] = runif_gen(rnd_engine);
  std::cerr << "random generation: " << time_since(tm_before) << " ms" << std::endl;

  // 2. compute all pairwise scalar products:
  time_since(tm_before);
  result_type avg = 0;
  for (int i = 0; i < N; ++i)
    for (int j = 0; j < N; ++j) 
      avg += scalar_product(xs[i], xs[j]);
  avg = avg / N*N;
  auto time = time_since(tm_before);
  std::cout << "result: " << avg << std::endl;
  std::cout << "time: " << time << " ms" << std::endl;
}

And here the D version:

import std.stdio;
import std.datetime;
import std.random;

const long N = 20000;
const int size = 10;

alias int value_type;
alias long result_type;
alias value_type[] vector_t;
alias uint size_type;

value_type scalar_product(const ref vector_t x, const ref vector_t y) {
  value_type res = 0;
  size_type siz = x.length;
  for (size_type i = 0; i < siz; ++i)
    res += x[i] * y[i];
  return res;
}

int main() {   
  auto tm_before = Clock.currTime();

  // 1. allocate and fill randomly many short vectors
  vector_t[] xs;
  xs.length = N;
  for (int i = 0; i < N; ++i) {
    xs[i].length = size;
  }
  writefln("allocation: %i ", (Clock.currTime() - tm_before));
  tm_before = Clock.currTime();

  for (int i = 0; i < N; ++i)
    for (int j = 0; j < size; ++j)
      xs[i][j] = uniform(-1000, 1000);
  writefln("random: %i ", (Clock.currTime() - tm_before));
  tm_before = Clock.currTime();

  // 2. compute all pairwise scalar products:
  result_type avg = cast(result_type) 0;
  for (int i = 0; i < N; ++i)
    for (int j = 0; j < N; ++j) 
      avg += scalar_product(xs[i], xs[j]);
  avg = avg / N*N;
  writefln("result: %d", avg);
  auto time = Clock.currTime() - tm_before;
  writefln("scalar products: %i ", time);

  return 0;
}

To enable all optimizations and disable all safety checks, compile your D program with the following DMD flags:

-O -inline -release -noboundscheck

EDIT: I've tried your programs with g++, dmd and gdc. dmd does lag behind, but gdc achieves performance very close to g++. The commandline I used was gdmd -O -release -inline (gdmd is a wrapper around gdc which accepts dmd options).

Looking at the assembler listing, it looks like neither dmd nor gdc inlined scalar_product, but g++/gdc did emit MMX instructions, so they might be auto-vectorizing the loop.