Compare speed of Julia and C++-11 for random walk simulations
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Random walk simulation in Julia and c++

This code compares the performance of Julia and C++-11 in a Monte Carlo simulation of a random walk. The Julia code runs three times faster than the C++ code.

The code records a statistic of a one dimensional random walker using the Mersenne Twister RNG.

Typical execution times (smaller is better):

  • 3.88s C++-11
  • 1.27s Julia v0.3
  • 1.68s Julia v0.4

This is similar to other testimonies: I heard about Julia and decided to test it. It takes a few minutes to learn, a few minutes to code an example, and the results are astonishing. I had been putting off moving from perl to python. Now, I don't have to. EDIT: There is a lot of technical software written in python. So in fact, calling python from Julia is very useful.

The result quoted uses the Julia code rwtest_simple.jl, and the c++ code The other code (not "simple") is more complicated as it tests how various changes affect effciency.

A caveat on interpreting the results. This does not support the general statement "Julia is faster than C++". In fact, in this particular example, the bulk of the time is spent generating random numbers. It means something more like: 1) Presently, Julia is much faster than the gnu C++-11 template library at sampling Boolean random variables. 2) If loops and function calls etc. are slower in Julia, the effect is very small compared to the relative efficiency of the RNGs.

Running the comparison

The idea is to write the code in a straightforward, idiomatic, yet efficient way. C++-11 improves on C and previous C++ versions by including high-quality RNG's in the interface to the standard library, although it is still a bit of a PITA to use them.

In linux/unix the test is done like this:

> c++ -std=c++11 -O3 -o rwtest_simple
> ./rwtest_simple > cout.txt
> julia rwtest_simple.jl > jout.txt

(The outputs are histograms that can be plotted.)

Using the g++ flag -march=native results in slower code.

The efficiency of the timed portion of both programs depends only on the speed of generating random numbers, checking the result, and incrementing counters. It is certainly possible to write a faster C/C++ program; but that requires more effort and trial and error, and probably abandoning the standard library. I already tried several ways to increase the speed of the C++ code.


  • c++ (Debian 4.9.1-15) 4.9.1

  • julia version 0.3.4-pre+30 (and later v0.3 versions)

There are also files rwtest.jl and, which are slightly more complicated. In particular, rwtest.jl works with the development versions, v0.4, of Julia as well as v0.3. p