Enz*_*ano 6 c++ parallel-processing openmp c++17
在性能关键的并行代码中,我有一个向量,其元素是:
目前,我预先计算了向量的所有可能值,因此竞争条件不应成为问题.为了提高性能,我正在考虑创建一个惰性向量,这样代码只在请求向量元素时执行计算.在并行区域中,可能会发生多个线程同时请求并且可能同时计算相同元素的情况.我如何处理这种可能的竞争条件？
下面是我想要实现的一个例子.它在Windows 10,Visual Studio 17下编译和运行.我使用C++ 17.
// Lazy.cpp : Defines the entry point for the console application.
#include "stdafx.h"
#include <vector>
#include <iostream>
#include <stdlib.h>
#include <chrono>
#include <math.h>
const double START_SUM = 1;
const double END_SUM = 1000;
//base object responsible for providing the values
class Evaluator
{
public:
Evaluator() {};
~Evaluator() {};
//Function with deterministic output, depending on the position
virtual double expensiveFunction(int pos) const = 0;
};
//
class EvaluatorA: public Evaluator
{
public:
//expensive evaluation
virtual double expensiveFunction(int pos) const override {
double t = 0;
for (int j = START_SUM; j++ < END_SUM; j++)
t += log(exp(log(exp(log(j + pos)))));
return t;
}
EvaluatorA() {};
~EvaluatorA() {};
};
class EvaluatorB : public Evaluator
{
public:
//even more expensive evaluation
virtual double expensiveFunction(int pos) const override {
double t = 0;
for (int j = START_SUM; j++ < 10*END_SUM; j++)
t += log(exp(log(exp(log(j + pos)))));
return t;
}
EvaluatorB() {};
~EvaluatorB() {};
};
class LazyVectorTest //vector that contains N possible results
{
public:
LazyVectorTest(int N,const Evaluator & eval) : N(N), innerContainer(N, 0), isThatComputed(N, false), eval_ptr(&eval)
{};
~LazyVectorTest() {};
//reset, to generate a new table of values
//the size of the vector stays constant
void reset(const Evaluator & eval) {
this->eval_ptr = &eval;
for (int i = 0; i<N; i++)
isThatComputed[i] = false;
}
int size() { return N; }
//accessing the same position should yield the same result
//unless the object is resetted
const inline double& operator[](int pos) {
if (!isThatComputed[pos]) {
innerContainer[pos] = eval_ptr->expensiveFunction(pos);
isThatComputed[pos] = true;
}
return innerContainer[pos];
}
private:
const int N;
const Evaluator* eval_ptr;
std::vector<double> innerContainer;
std::vector<bool> isThatComputed;
};
//the parallel access will take place here
template <typename T>
double accessingFunction(T& A, const std::vector<int>& elementsToAccess) {
double tsum = 0;
int size = elementsToAccess.size();
//#pragma omp parallel for
for (int i = 0; i < size; i++)
tsum += A[elementsToAccess[i]];
return tsum;
}
std::vector<int> randomPos(int sizePos, int N) {
std::vector<int> elementsToAccess;
for (int i = 0; i < sizePos; i++)
elementsToAccess.push_back(rand() % N);
return elementsToAccess;
}
int main()
{
srand(time(0));
int minAccessNumber = 1;
int maxAccessNumber = 100;
int sizeVector = 50;
auto start = std::chrono::steady_clock::now();
double res = 0;
float numberTest = 100;
typedef LazyVectorTest container;
EvaluatorA eval;
for (int i = 0; i < static_cast<int>(numberTest); i++) {
res = eval.expensiveFunction(i);
}
auto end = std::chrono::steady_clock::now();
std::chrono::duration<double, std::milli>diff(end - start);
double benchmark = diff.count() / numberTest;
std::cout <<"Average time to compute expensive function:" <<benchmark<<" ms"<<std::endl;
std::cout << "Value of the function:" << res<< std::endl;
std::vector<std::vector<int>> indexs(numberTest);
container A(sizeVector, eval);
for (int accessNumber = minAccessNumber; accessNumber < maxAccessNumber; accessNumber++) {
indexs.clear();
for (int i = 0; i < static_cast<int>(numberTest); i++) {
indexs.emplace_back(randomPos(accessNumber, sizeVector));
}
auto start_lazy = std::chrono::steady_clock::now();
for (int i = 0; i < static_cast<int>(numberTest); i++) {
A.reset(eval);
double res_lazy = accessingFunction(A, indexs[i]);
}
auto end_lazy = std::chrono::steady_clock::now();
std::chrono::duration<double, std::milli>diff_lazy(end_lazy - start_lazy);
std::cout << accessNumber << "," << diff_lazy.count() / numberTest << ", " << diff_lazy.count() / (numberTest* benchmark) << std::endl;
}
return 0;
}
您当前的代码有问题，主要是因为std::vector<bool> 太糟糕了，而且还缺少原子性和内存一致性。这是完全基于 OpenMP 的解决方案的草图。我建议实际上对丢失的条目进行特殊标记，而不是单独标记vector<bool>- 它使一切变得更加容易：
class LazyVectorTest //vector that contains N possible results
{
public:
LazyVectorTest(int N,const Evaluator & eval) : N(N), innerContainer(N, invalid), eval_ptr(&eval)
{};
~LazyVectorTest() {};
//reset, to generate a new table of values
//the size of the vector stays constant
void reset(const Evaluator & eval) {
this->eval_ptr = &eval;
for (int i = 0; i<N; i++) {
// Use atomic if that could possible be done in parallel
// omit that for performance if you doun't ever run it in parallel
#pragma omp atomic write
innerContainer[i] = invalid;
}
// Flush to make sure invalidation is visible to all threads
#pragma omp flush
}
int size() { return N; }
// Don't return a reference here
double operator[] (int pos) {
double value;
#pragma omp atomic read
value = innerContainer[pos];
if (value == invalid) {
value = eval_ptr->expensiveFunction(pos);
#pragma omp atomic write
innerContainer[pos] = value;
}
return value;
}
private:
// Use nan, inf or some random number - doesn't really matter
static constexpr double invalid = std::nan("");
const int N;
const Evaluator* eval_ptr;
std::vector<double> innerContainer;
};
如果发生冲突，其他线程将只是冗余地计算该值。- 利用确定性本质。我omp atomic在元素的读取和写入上使用，可以确保不会读取不一致的“半写”值。
对于罕见的不良情况，此解决方案可能会产生一些额外的延迟。反过来，好的情况是最优的，只需一次原子读取。你甚至不需要任何内存flushes / seq_cst- 最坏的情况是冗余计算。如果单独编写标志和值，则需要这些（顺序一致性），以确保更改可见的顺序正确。