高性能并行编程与优化 | 第04讲回家作业

Posted 2022-02-22 yantuguiguziPGJ

目录

一 题目4

二 原来代码运行结果

 三  优秀的代码答案

四 优秀的运行结果和解答

---

 

一 题目4

# 高性能并行编程与优化 - 第04讲的回家作业

通过 pull request 提交作业。会批分数，但是：

没有结业证书，回家作业仅仅作为评估学习效果和巩固知识的手段，不必为分数感到紧张 :)
量力而行，只要能在本课中，学到昨天的自己不懂的知识，就是胜利，没必要和别人攀比。
注意不要偷看别人的作业哦！

- 课件：https://github.com/parallel101/course
- 录播：https://space.bilibili.com/263032155

作业提交时间不限 :) 即使完结了还想交的话我也会看的~ 不过最好在下一讲开播前完成。

- 如何开 pull request：https://zhuanlan.zhihu.com/p/51199833
- 如何设置 https 代理：https://www.jianshu.com/p/b481d2a42274

## 评分规则

- 在你的电脑上加速了多少倍，就是多少分！请在 PR 描述中写明加速前后的用时数据。
- 最好详细解释一下为什么这样可以优化。会额外以乘法的形式加分。
- 比如你优化后加速了 50 倍，讲的很详细，所以分数乘 2，变成 100 分！
- 比如你优化后加速了 1000 倍，但是你的 PR 描述是空，所以分数乘 0，变成 0 分！

## 作业要求

利用这次课上所学知识，修改 main.cpp，优化其中的多体引力求解器：

- 不允许使用多线程并行
- 不允许做算法复杂度优化
- 可以针对编译器和平台优化，这次不要求跨平台
- 可以用 xmmintrin.h，如果你觉得编译器靠不住的话
 

#include <cstdio>
#include <cstdlib>
#include <vector>
#include <chrono>
#include <cmath>

float frand() 
    return (float)rand() / RAND_MAX * 2 - 1;


struct Star 
    float px, py, pz;
    float vx, vy, vz;
    float mass;
;

std::vector<Star> stars;

void init() 
    for (int i = 0; i < 48; i++) 
        stars.push_back(
            frand(), frand(), frand(),
            frand(), frand(), frand(),
            frand() + 1,
        );
    


float G = 0.001;
float eps = 0.001;
float dt = 0.01;

void step() 
    for (auto &star: stars) 
        for (auto &other: stars) 
            float dx = other.px - star.px;
            float dy = other.py - star.py;
            float dz = other.pz - star.pz;
            float d2 = dx * dx + dy * dy + dz * dz + eps * eps;
            d2 *= sqrt(d2);
            star.vx += dx * other.mass * G * dt / d2;
            star.vy += dy * other.mass * G * dt / d2;
            star.vz += dz * other.mass * G * dt / d2;
        
    
    for (auto &star: stars) 
        star.px += star.vx * dt;
        star.py += star.vy * dt;
        star.pz += star.vz * dt;
    


float calc() 
    float energy = 0;
    for (auto &star: stars) 
        float v2 = star.vx * star.vx + star.vy * star.vy + star.vz * star.vz;
        energy += star.mass * v2 / 2;
        for (auto &other: stars) 
            float dx = other.px - star.px;
            float dy = other.py - star.py;
            float dz = other.pz - star.pz;
            float d2 = dx * dx + dy * dy + dz * dz + eps * eps;
            energy -= other.mass * star.mass * G / sqrt(d2) / 2;
        
    
    return energy;


template <class Func>
long benchmark(Func const &func) 
    auto t0 = std::chrono::steady_clock::now();
    func();
    auto t1 = std::chrono::steady_clock::now();
    auto dt = std::chrono::duration_cast<std::chrono::milliseconds>(t1 - t0);
    return dt.count();


int main() 
    init();
    printf("Initial energy: %f\\n", calc());
    auto dt = benchmark([&] 
        for (int i = 0; i < 100000; i++)
            step();
    );
    printf("Final energy: %f\\n", calc());
    printf("Time elapsed: %ld ms\\n", dt);
    return 0;

二 原来代码运行结果

 三  优秀的代码答案

//参考代码：https://github.com/parallel101/hw04/pull/3

#include <cstdio>
#include <cstdlib>
#include <vector>
#include <chrono>
#include <cmath>
#include <iostream>
#include <immintrin.h>
#include <cassert>

float frand() 
    return (float)rand() / RAND_MAX * 2 - 1;


constexpr std::size_t N = 48;
constexpr float G = 0.001f;
constexpr float eps = 0.001f;
constexpr float dt = 0.01f;
constexpr float eps_sqr = eps * eps;
constexpr float G_dt = G * dt;

template<std::size_t N>
struct Star 
    //float px, py, pz;
    //float vx, vy, vz;
    //float mass;
    alignas(64) float px[N], py[N], pz[N];
    alignas(64) float vx[N], vy[N], vz[N];
    alignas(64) float mass[N];
;

//std::vector<Star> stars;
Star<N> stars;
//void init() 
//    for (int i = 0; i < 48; i++) 
//        stars.push_back(
//            frand(), frand(), frand(),
//            frand(), frand(), frand(),
//            frand() + 1,
//        );
void init() 
    for (std::size_t i; i < N; ++i) 
        stars.px[i] = frand(), stars.py[i] = frand(), stars.pz[i] = frand();
        stars.vx[i] = frand(), stars.vy[i] = frand(), stars.vz[i] = frand();
        stars.mass[i] = frand() + 1;
    


//float G = 0.001;
//float eps = 0.001;
//float dt = 0.01;
//
//void step() 
//    for (auto &star: stars) 
//        for (auto &other: stars) 
//            float dx = other.px - star.px;
//            float dy = other.py - star.py;
//            float dz = other.pz - star.pz;
//            float d2 = dx * dx + dy * dy + dz * dz + eps * eps;
//            d2 *= sqrt(d2);
//            star.vx += dx * other.mass * G * dt / d2;
//            star.vy += dy * other.mass * G * dt / d2;
//            star.vz += dz * other.mass * G * dt / d2;
//        
using fsimd_t = __m256;
inline auto set1(float f)  return _mm256_set1_ps(f); 
inline auto load(const float* f)  return _mm256_load_ps(f); 
inline void store(float* f, fsimd_t a)  _mm256_store_ps(f, a); 
inline auto add(fsimd_t a, fsimd_t b)  return _mm256_add_ps(a, b); 
inline auto sub(fsimd_t a, fsimd_t b)  return _mm256_sub_ps(a, b); 
inline auto mul(fsimd_t a, fsimd_t b)  return _mm256_mul_ps(a, b); 
inline auto div(fsimd_t a, fsimd_t b)  return _mm256_div_ps(a, b); 
inline auto sqrt(fsimd_t a)  return _mm256_sqrt_ps(a); 
inline auto rsqrt(fsimd_t a)  return _mm256_rsqrt_ps(a); 
inline auto rcp(fsimd_t a)  return _mm256_rcp_ps(a); 

void step_avx() 

    auto eps_sqr8 = set1(eps_sqr);

    for (std::size_t j; j < N; ++j) 
        auto mg_dt = set1(G * stars.mass[j] * dt);
        auto xj = set1(stars.px[j]);
        auto yj = set1(stars.py[j]);
        auto zj = set1(stars.pz[j]);

        auto unroll_body = [&](std::size_t i) 
            auto xi = load(&stars.px[i]);
            auto yi = load(&stars.py[i]);
            auto zi = load(&stars.pz[i]);

            auto dx = sub(xj, xi);
            auto dy = sub(yj, yi);
            auto dz = sub(zj, zi);

            auto x2 = mul(dx, dx);
            auto y2 = mul(dy, dy);
            auto z2 = mul(dz, dz);

            auto d2 = add(add(x2, y2), add(z2, eps_sqr8));
            auto inv_d2 = rcp(d2);
            auto inv_d = rsqrt(d2);
            auto mg_dt_invd3 = mul(mul(mg_dt, inv_d2), inv_d);

            auto vx = load(&stars.vx[i]);
            auto vy = load(&stars.vy[i]);
            auto vz = load(&stars.vz[i]);

            auto new_vx = add(mul(mg_dt_invd3, dx), vx);
            auto new_vy = add(mul(mg_dt_invd3, dy), vy);
            auto new_vz = add(mul(mg_dt_invd3, dz), vz);

            store(&stars.vx[i], new_vx);
            store(&stars.vy[i], new_vy);
            store(&stars.vz[i], new_vz);
        ;

        unroll_body(0);
        unroll_body(8);
        unroll_body(16);
        unroll_body(24);
        unroll_body(32);
        unroll_body(40);

        // for(std::size_t i ; i < N ; i += 8)
        //     unroll_body(i);
        // 
    
    //for (auto &star: stars) 
    //    star.px += star.vx * dt;
    //    star.py += star.vy * dt;
    //    star.pz += star.vz * dt;

    for (std::size_t i; i < N; i += 8) 
        auto dt8 = set1(dt);
        auto vx = load(&stars.vx[i]);
        auto vy = load(&stars.vy[i]);
        auto vz = load(&stars.vz[i]);

        auto new_px = add(load(&stars.px[i]), mul(vx, dt8));
        auto new_py = add(load(&stars.py[i]), mul(vy, dt8));
        auto new_pz = add(load(&stars.pz[i]), mul(vz, dt8));

        store(&stars.px[i], new_px);
        store(&stars.py[i], new_py);
        store(&stars.pz[i], new_pz);
    


float calc() 
    float energy = 0;
    //for (auto &star: stars) 
    //    float v2 = star.vx * star.vx + star.vy * star.vy + star.vz * star.vz;
    //    energy += star.mass * v2 / 2;
    //    for (auto &other: stars) 
    //        float dx = other.px - star.px;
    //        float dy = other.py - star.py;
    //        float dz = other.pz - star.pz;
    //        float d2 = dx * dx + dy * dy + dz * dz + eps * eps;
    //        energy -= other.mass * star.mass * G / sqrt(d2) / 2;
    for (std::size_t i; i < N; ++i) 
        float v2 = stars.vx[i] * stars.vx[i] + stars.vy[i] * stars.vy[i] + stars.vz[i] * stars.vz[i];
        energy += stars.mass[i] * v2 / 2;

        for (std::size_t j; j < N; ++j) 
            float dx = stars.px[j] - stars.px[i];
            float dy = stars.py[j] - stars.py[i];
            float dz = stars.pz[j] - stars.pz[i];
            float d2 = dx * dx + dy * dy + dz * dz + eps * eps;
            energy -= stars.mass[j] * stars.mass[i] * G / std::sqrt(d2) / 2;
        
    
    return energy;


template <class Func>
//long benchmark(Func const &func) 
auto benchmark(Func const& func) 
    auto t0 = std::chrono::steady_clock::now();
    func();
    auto t1 = std::chrono::steady_clock::now();
    auto dt = std::chrono::duration_cast<std::chrono::milliseconds>(t1 - t0);
    return dt.count();


int main() 
    init();
    //printf("Initial energy: %f\\n", calc());
    std::cout << "Initial energy: " << calc() << std::endl;
    auto dt = benchmark([&] 
        for (int i = 0; i < 100000; i++)
            //step();
            step_avx();
    );
    //printf("Final energy: %f\\n", calc());
    //printf("Time elapsed: %ld ms\\n", dt);
    std::cout << "Final energy: " << calc() << std::endl;
    std::cout << "Time elapsed: " << dt << " ms" << std::endl;
    system("PAUSE");
    return 0;

cmake_minimum_required(VERSION 3.12)
project(hellocmake LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 17)
if (NOT CMAKE_BUILD_TYPE)
    set(CMAKE_BUILD_TYPE Release)
endif()

add_executable(main main.cpp)
if(MSVC)
    target_compile_options(main PUBLIC /arch:AVX2 /fp:fast /O2 /openmp:experimental)
else()
    target_compile_options(main PUBLIC -ffast-math -march=native -O3) 
endif()

BUILD_TPYE=RelWithDebInfo

cmake . -B build \\
    -DCMAKE_TOOLCHAIN_FILE=C:/zeno/vcpkg/scripts/buildsystems/vcpkg.cmake \\
    -DVCPKG_TARGET_TRIPLET=x64-windows-static \\
    -DCMAKE_MSVC_RUNTIME_LIBRARY="MultiThreaded$<$<CONFIG:Debug>:Debug>"

cmake --build build --config $BUILD_TPYE

echo "================= Baseline =================="
./baseline

echo "================= Optimized ================="
./build/$BUILD_TPYE/main.exe

echo "================== Finish ===================" 

四 优秀的运行结果和解答

 

Fin with AVX2 by JYLeeLYJ · Pull Request #3 · parallel101/hw04 (github.com)https://github.com/parallel101/hw04/pull/3