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前面两篇让代码飞起来——高性能 Julia 学习笔记（一）让代码飞起来——高性能 Julia 学习笔记（二），介绍了如何写出高性能的 Julia 代码，这篇结合我最近的项目，简单测试对比一下各种语言用 monte carlo 算法计算 pi 的效率。
首先声明一下，本文不能算严格意义上的性能测试，也不想挑起语言圣战，个人能力有限，实现的不同语言版本代码也未必是最高效的，基本都是 naive 实现。
如果对 Monte Carlo 算法不熟悉，可以参考下面两个资料，我就不浪费时间重复了：
https://zh.wikipedia.org/wiki…
http://www.ruanyifeng.com/blo…
机器是 2015 年的 MacPro：
Processor: 2.5GHz Intel Core i7
Memory: 16GB 1600 MHZ DDR3
Os: macOS High Sierra Version 10.13.4
JS 版本
function pi(n) {
let inCircle = 0;
for (let i = 0; i <= n; i++) {
x = Math.random();
y = Math.random();
if (x * x + y * y < 1.0) {
inCircle += 1;
}
}
return (4.0 * inCircle) / n;
}
const N = 100000000;
console.log(pi(N));
结果：
➜ me.magicly.performance git:(master) ✗ node –version
v10.11.0
➜ me.magicly.performance git:(master) ✗ time node mc.js
3.14174988
node mc.js 10.92s user 0.99s system 167% cpu 7.091 total
Go 版本
package main
import (
“math/rand”
)
func PI(samples int) (result float64) {
inCircle := 0
r := rand.New(rand.NewSource(42))
for i := 0; i < samples; i++ {
x := r.Float64()
y := r.Float64()
if (x*x + y*y) < 1 {
inCircle++
}
}
return float64(inCircle) / float64(samples) * 4.0
}
func main() {
samples := 100000000
PI(samples)
}
结果：
➜ me.magicly.performance git:(master) ✗ go version
go version go1.11 darwin/amd64
➜ me.magicly.performance git:(master) ✗ time go run monte_carlo.go
go run monte_carlo.go 2.17s user 0.10s system 101% cpu 2.231 total
C 版本
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#define SEED 42
int main(int argc, char **argv)
{
int niter = 100000000;
double x, y;
int i, count = 0;
double z;
double pi;
srand(SEED);
count = 0;
for (i = 0; i < niter; i++)
{
x = (double)rand() / RAND_MAX;
y = (double)rand() / RAND_MAX;
z = x * x + y * y;
if (z <= 1)
count++;
}
pi = (double)count / niter * 4;
printf(“# of trials= %d , estimate of pi is %g \n”, niter, pi);
}
结果：
➜ me.magicly.performance git:(master) ✗ gcc –version
Configured with: –prefix=/Applications/Xcode.app/Contents/Developer/usr –with-gxx-include-dir=/usr/include/c++/4.2.1
Apple LLVM version 9.1.0 (clang-902.0.39.2)
Target: x86_64-apple-darwin17.5.0
Thread model: posix
InstalledDir: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
➜ me.magicly.performance git:(master) ✗ gcc -O2 -o mc-pi-c mc-pi.c
➜ me.magicly.performance git:(master) ✗ time ./mc-pi-c
# of trials= 100000000 , estimate of pi is 3.14155
./mc-pi-c 1.22s user 0.00s system 99% cpu 1.226 total
C++ 版本
#include <iostream>
#include <cstdlib> //defines rand(), srand(), RAND_MAX
#include <cmath> //defines math functions
using namespace std;
int main()
{
const int SEED = 42;
int interval, i;
double x, y, z, pi;
int inCircle = 0;
srand(SEED);
const int N = 100000000;
for (i = 0; i < N; i++)
{
x = (double)rand() / RAND_MAX;
y = (double)rand() / RAND_MAX;
z = x * x + y * y;
if (z < 1)
{
inCircle++;
}
}
pi = double(4 * inCircle) / N;
cout << “\nFinal Estimation of Pi = ” << pi << endl;
return 0;
}
结果：
➜ me.magicly.performance git:(master) ✗ c++ –version
Apple LLVM version 9.1.0 (clang-902.0.39.2)
Target: x86_64-apple-darwin17.5.0
Thread model: posix
InstalledDir: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
➜ me.magicly.performance git:(master) ✗ c++ -O2 -o mc-pi-cpp mc-pi.cpp
➜ me.magicly.performance git:(master) ✗ time ./mc-pi-cpp
Final Estimation of Pi = 3.14155
./mc-pi-cpp 1.23s user 0.01s system 99% cpu 1.239 total
Julia 版本
function pi(N::Int)
inCircle = 0
for i = 1:N
x = rand() * 2 – 1
y = rand() * 2 – 1
r2 = x*x + y*y
if r2 < 1.0
inCircle += 1
end
end
return inCircle / N * 4.0
end
N = 100_000_000
println(pi(N))
结果：
➜ me.magicly.performance git:(master) ✗ julia
_
_ _ _(_)_ | Documentation: https://docs.julialang.org
(_) | (_) (_) |
_ _ _| |_ __ _ | Type “?” for help, “]?” for Pkg help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 1.0.1 (2018-09-29)
_/ |\__’_|_|_|\__’_| | Official https://julialang.org/ release
|__/ |
julia> versioninfo()
Julia Version 1.0.1
Commit 0d713926f8 (2018-09-29 19:05 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin14.5.0)
CPU: Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.0 (ORCJIT, haswell)
➜ me.magicly.performance git:(master) ✗ time julia mc.jl
3.14179496
julia mc.jl 0.85s user 0.17s system 144% cpu 0.705 total
另外 Rust 开发环境升级搞出了点问题，没弄好，不过根据之前的经验，我估计跟 C++ 差不多。
github 上找到一份对比，包含了更多的语言，有兴趣的可以参考一下 https://gist.github.com/jmoir…，LuaJIT 居然跟 Rust 差不多一样快，跟 Julia 官网的 benchmark 比较一致 https://julialang.org/benchma…。
另外实现了两个 Go 的并发版本：
package main
import (
“fmt”
“math/rand”
“runtime”
“time”
)
type Job struct {
n int
}
var threads = runtime.NumCPU()
var rands = make([]*rand.Rand, 0, threads)
func init() {
fmt.Printf(“cpus: %d\n”, threads)
runtime.GOMAXPROCS(threads)
for i := 0; i < threads; i++ {
rands = append(rands, rand.New(rand.NewSource(time.Now().UnixNano())))
}
}
func MultiPI2(samples int) float64 {
t1 := time.Now()
threadSamples := samples / threads
jobs := make(chan Job, 100)
results := make(chan int, 100)
for w := 0; w < threads; w++ {
go worker2(w, jobs, results, threadSamples)
}
go func() {
for i := 0; i < threads; i++ {
jobs <- Job{
n: i,
}
}
close(jobs)
}()
var total int
for i := 0; i < threads; i++ {
total += <-results
}
result := float64(total) / float64(samples) * 4
fmt.Printf(“MultiPI2: %d times, value: %f, cost: %s\n”, samples, result, time.Since(t1))
return result
}
func worker2(id int, jobs <-chan Job, results chan<- int, threadSamples int) {
for range jobs {
// fmt.Printf(“worker id: %d, job: %v, remain jobs: %d\n”, id, job, len(jobs))
var inside int
// r := rand.New(rand.NewSource(time.Now().UnixNano()))
r := rands[id]
for i := 0; i < threadSamples; i++ {
x, y := r.Float64(), r.Float64()
if x*x+y*y <= 1 {
inside++
}
}
results <- inside
}
}
func MultiPI(samples int) float64 {
t1 := time.Now()
threadSamples := samples / threads
results := make(chan int, threads)
for j := 0; j < threads; j++ {
go func() {
var inside int
r := rand.New(rand.NewSource(time.Now().UnixNano()))
for i := 0; i < threadSamples; i++ {
x, y := r.Float64(), r.Float64()
if x*x+y*y <= 1 {
inside++
}
}
results <- inside
}()
}
var total int
for i := 0; i < threads; i++ {
total += <-results
}
result := float64(total) / float64(samples) * 4
fmt.Printf(“MultiPI: %d times, value: %f, cost: %s\n”, samples, result, time.Since(t1))
return result
}
func PI(samples int) (result float64) {
t1 := time.Now()
var inside int = 0
r := rand.New(rand.NewSource(time.Now().UnixNano()))
for i := 0; i < samples; i++ {
x := r.Float64()
y := r.Float64()
if (x*x + y*y) < 1 {
inside++
}
}
ratio := float64(inside) / float64(samples)
result = ratio * 4
fmt.Printf(“PI: %d times, value: %f, cost: %s\n”, samples, result, time.Since(t1))
return
}
func main() {
samples := 100000000
PI(samples)
MultiPI(samples)
MultiPI2(samples)
}
结果：
➜ me.magicly.performance git:(master) ✗ time go run monte_carlo.1.go
cpus: 8
PI: 100000000 times, value: 3.141778, cost: 2.098006252s
MultiPI: 100000000 times, value: 3.141721, cost: 513.008435ms
MultiPI2: 100000000 times, value: 3.141272, cost: 485.336029ms
go run monte_carlo.1.go 9.41s user 0.18s system 285% cpu 3.357 total
可以看出，效率提升了 4 倍。为什么明明有 8 个 CPU，只提升了 4 倍呢？其实我的 macpro 就是 4 核的，8 是超线程出来的虚拟核，在 cpu 密集计算上并不能额外提升效率。可以参考这篇文章：物理 CPU、CPU 核数、逻辑 CPU、超线程。
下一篇，我们就来看一下 Julia 中如何利用并行进一步提高效率。
欢迎加入知识星球一起分享讨论有趣的技术话题。
