A dumb microbenchmark: folding a 10000000 element list in OCaml vs C++ vs Python vs Go

gistfile1.txt

Aggregate results:

Wall time:
OCaml: 0.76s
C++ (gcc defaults): 1.45s
C++ (gcc with -O2): 0.62s
Python: 2.18s
Go: 3.3s

Memory consumption:
OCaml: 146MiB
C++: 230MiB
Python: 347MiB
Go: 190MiB


$ ocamlopt -o list_test_ml ./list_test.ml 

$ time ./list_test_ml 
50000005000000

real	0m0.763s
user	0m0.669s
sys	0m0.087s

$ time ./list_test_ml 
50000005000000

real	0m0.798s
user	0m0.720s
sys	0m0.071s

$ time ./list_test_ml 
50000005000000

real	0m0.723s
user	0m0.647s
sys	0m0.070s

$ g++ -o list_test_cpp ./list_test.cpp 

$ time ./list_test_cpp 
50000005000000
real	0m1.412s
user	0m1.316s
sys	0m0.091s

$ time ./list_test_cpp 
50000005000000
real	0m1.500s
user	0m1.397s
sys	0m0.097s

$ time ./list_test_cpp 
50000005000000
real	0m1.431s
user	0m1.332s
sys	0m0.093s
$ time ./list_test.py 
50000005000000

real	0m2.127s
user	0m1.998s
sys	0m0.121s
$ time ./list_test.py 
50000005000000

real	0m2.282s
user	0m2.149s
sys	0m0.124s
$ time ./list_test.py 
50000005000000

real	0m2.142s
user	0m2.007s
sys	0m0.127s

$ go build list-test.go

$ time ./list-test 
50000005000000

real	0m3.384s
user	0m3.621s
sys	0m0.249s
$ time ./list-test 
50000005000000

real	0m3.136s
user	0m4.035s
sys	0m0.243s
$ time ./list-test 
50000005000000

real	0m3.382s
user	0m3.583s
sys	0m0.256s


# valgrind calls
$ valgrind --tool=massif ./list_test_ml
$valgrind --tool=massif ./list_test_cpp
$ valgrind --tool=massif python3 ./list_test.py

# Peak memory usage viewed with massif-visualizer
OCaml: 146MiB
C++: 230MiB
Python: 347MiB

Valgrind doesn't work for go, profiling with pprof tool gives:
$ go tool pprof --text mem.pprof 
190.47MB of 190.47MB total (  100%)
      flat  flat%   sum%        cum   cum%
  190.47MB   100%   100%   190.47MB   100%  

list-test.go

package main

import "container/list";
import "fmt"

func main() {
    first := 0;
    last := 10000000;

    l := list.New();

    // build the list
    for i := first; i <= last; i = i + 1 {
        l.PushFront(i);
    }

    // fold the list
    sum := 0
    for e := l.Front(); e != nil; e = e.Next() {
        sum = sum + e.Value.(int)
    }

    fmt.Println(sum)
}

list_test.cpp

#include <iostream>
#include <list>

int main(void)
{
    int first = 0;
    int last = 10000000;

    std::list<int> l;

    for(int x=first;x<=last;x++)
    {
        l.push_front(x); // push_front is O(1)
    }

    long sum = 0;
    for (std::list<int>::const_iterator i = l.begin(), end = l.end(); i != end; ++i)
    {
        sum += *i;
    }

    std::cout << sum;

    return(0);
}

list_test.ml

let first = 0
let last = 10000000

let make_list first last =
  let rec aux xs n =
    if n >= last then xs
    else
      let next = n + 1 in aux (next :: xs) next (* :: aka cons is O(1) *)
      in aux [] first

(* If my memory hadn't failed me I wouldn't reimplement it, List.fold_left is already tail recursive *)
let fold f a xs =
  let rec aux f acc ys =
    match ys with
    | [] -> acc
    | y :: ys ->
      let acc = f acc y in aux f acc ys
  in aux f a xs 

let () =
  let xs = make_list first last in
  print_endline @@ string_of_int (fold (+) 0 xs)

list_test.py

#!/usr/bin/env python3

first = 0
last = 10000000

def make_list(first, last):
    i = first
    l = []
    while i <= last:
        l.append(i) # append is O(1)
        i = i + 1
    return l

def sum_list(list):
    sum = 0
    for i in list:
        sum = sum + i
    return sum

l = make_list(first, last)
print(sum(l))

Interesting results, although I think there are better ways to write the Python program.

Here is some Python code that does exactly the same thing:

#!/usr/bin/python3
print(sum(list(range(10000001))))

In this code I deliberately created a list before summation so it would somewhat resemble your code algorithmically. If I forget about the list and sum the iterator directly it is even faster:

#!/usr/bin/python3
print(sum(range(10000001)))

For this test your OCaml program was built with ocamlbuild list_test.native.

Here are the numbers on my computer averaged over 20 runs:

Average run time for OCaml native: 0.5426s
Average run time for Python original: 1.1347s
Average run time for Python v1: 0.3274s
Average run time for Python v2: 0.1854s

The timing script I used:

#!/usr/bin/python3
import time
import subprocess


def time_prog(cmd):
    t0 = time.time()
    p = subprocess.Popen([cmd], stdout=subprocess.PIPE)
    p.wait()
    t1 = time.time()
    Δt = t1 - t0
    print(p.stdout.read().decode('utf8').strip())  # sanity check
    return Δt


progs = {'OCaml native': '/tmp/list_test.native',
         'Python original': '/tmp/list_test_orig.py',
         'Python v1': '/tmp/list_test.py',
         'Python v2': '/tmp/list_test_1.py'}
results = {}
N = 20
for prog, cmd in progs.items():
    results[prog] = []
    for _ in range(N):
        Δt = time_prog(cmd)
        results[prog].append(Δt)

for prog, Δts in results.items():
    print("Average run time for {}: {}s"
          .format(prog, round(sum(Δts) / N, 4)))

Interesting results, although I think there are better ways to write the Python program.

Here is some Python code that does exactly the same thing:

#!/usr/bin/python3
print(sum(list(range(10000001))))

In this code I deliberately created a list before summation so it would somewhat resemble your code algorithmically. If I forget about the list and sum the iterator directly it is even faster:

#!/usr/bin/python3
print(sum(range(10000001)))

For this test your OCaml program was built with ocamlbuild list_test.native.

Here are the numbers on my computer averaged over 20 runs:

Average run time for OCaml native: 0.5426s
Average run time for Python original: 1.1347s
Average run time for Python v1: 0.3274s
Average run time for Python v2: 0.1854s

The timing script I used:

#!/usr/bin/python3
import time
import subprocess


def time_prog(cmd):
    t0 = time.time()
    p = subprocess.Popen([cmd], stdout=subprocess.PIPE)
    p.wait()
    t1 = time.time()
    Δt = t1 - t0
    print(p.stdout.read().decode('utf8').strip())  # sanity check
    return Δt


progs = {'OCaml native': '/tmp/list_test.native',
         'Python original': '/tmp/list_test_orig.py',
         'Python v1': '/tmp/list_test.py',
         'Python v2': '/tmp/list_test_1.py'}
results = {}
N = 20
for prog, cmd in progs.items():
    results[prog] = []
    for _ in range(N):
        Δt = time_prog(cmd)
        results[prog].append(Δt)

for prog, Δts in results.items():
    print("Average run time for {}: {}s"
          .format(prog, round(sum(Δts) / N, 4)))