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我在 https://github.com/spakai/threadpool_future 中有 3 个针对此代码 ThreadPool 的单元测试用例
class ThreadPoolTest : public Test {
public:
ThreadPool pool;
std::condition_variable wasExecuted;
std::mutex m;
std::vector<std::shared_ptr<std::thread>> threads;
unsigned int count{0};
void incrementCountAndNotify() {
std::unique_lock<std::mutex> lock(m);
++count;
std::cout << count << std::endl;
wasExecuted.notify_all();
}
void waitForNotificationOrFailOnTimeout(unsigned expectedCount, int milliseconds=80000) {
std::unique_lock<std::mutex> lock(m);
ASSERT_THAT(wasExecuted.wait_for(lock, std::chrono::milliseconds(milliseconds), [&] { return count == expectedCount; }), Eq(true));
}
bool hasDuplicates(const std::vector<int> & birthdays) {
std::set<int> uniqueBirthdays(birthdays.begin(), birthdays.end());
return (uniqueBirthdays.size() != birthdays.size());
}
std::vector<int> generateNumbers(const int popSize) {
std::vector<int> list;
std::random_device rd;
std::default_random_engine dre(rd());
std::uniform_int_distribution<int> di(0,365);
for(int i{0}; i < popSize ; i++) {
list.push_back(di(dre));
}
return list;
}
void TearDown() override {
for (auto& t: threads) t->join();
}
};
TEST_F(ThreadPoolTest,TimingTestWithFuture) {
pool.start(4);
std::vector<std::future<unsigned long long>> results;
auto work = [](int n) {
unsigned long long factorial = 1;
for(int i = 1; i <=n; ++i) {
factorial *= i;
}
return factorial;
};
TestTimer timer("4-sized-TP with Future",0);
for (int i = 5; i < 60 ; i++) {
results.push_back(pool.submit(work,i));
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i).get();
}
}
TEST_F(ThreadPoolTest,TimingTestWithCallback) {
pool.start(4);
std::vector<unsigned long long> results;
TestTimer timer("4-sized-TP-Callback",0);
for (int n = 5; n < 60 ; n++) {
auto work = [&]() {
unsigned long long factorial = 1;
for(int i = 1; i <=n; ++i) {
factorial *= i;
}
{
std::lock_guard<std::mutex> guard(m);
results.push_back(factorial);
}
incrementCountAndNotify();
};
pool.add(work);
}
waitForNotificationOrFailOnTimeout(55);
}
TEST_F(ThreadPoolTest,TimingTestWithoutTP) {
std::vector<unsigned long long> results;
auto work = [](int n) {
unsigned long long factorial = 1;
for(int i = 1; i <=n; ++i) {
factorial *= i;
}
return factorial;
};
TestTimer timer("In Sequence",0);
for (int i = 5; i < 60 ; i++) {
results.push_back(work(i));
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i);
}
}
我在一台 4 CPU 机器上运行。我得到的计时结果显示单线程最快,而返回 future 的线程最慢。
4-sized-TP with Future Time taken = 2.364ms
4-sized-TP-Callback 所用时间 = 1.103ms
按顺序花费的时间 = 0.026 毫秒
我原以为时间顺序会倒过来。我做测试的方式是错误的吗?还是我的代码?
新测试会占用 CPU 资源
TEST_F(ThreadPoolTest,BirthdayParadoxInSequenceTimingTest) {
std::vector<int> results;
TestTimer timer("Birthday Paradox :: In Sequence",0);
std::vector<int> popList = {10,23,30,40,50,60,70,80,90,100,120,150};
for(auto it=popList.begin(); it!=popList.end(); ++it) {
int id = *it;
int dup{0};
for(int i{0}; i< 100000; i++) {
auto list = generateNumbers(id);
if(hasDuplicates(list)) ++dup;
}
results.push_back(dup);
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i);
}
}
TEST_F(ThreadPoolTest,BirthdayParadoxTPWithFutureTimingTest) {
std::vector<int> popList = {10,23,30,40,50,60,70,80,90,100,120,150};
pool.start(4);
std::vector<std::future<int>> results;
TestTimer timer("4-sized-TP with Future",0);
for(auto it=popList.begin(); it!=popList.end(); ++it) {
int id = *it;
auto work = [&](int pop) {
int dup{0};
for(int i{0}; i < 100000 ; i++) {
auto list = generateNumbers(pop);
if(hasDuplicates(list)) ++dup;
}
return dup;
};
results.push_back(pool.submit(work,id));
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i).get();
}
}
TEST_F(ThreadPoolTest,BirthdayParadoxTPWithCallBackTimingTest) {
std::vector<int> popList = {10,23,30,40,50,60,70,80,90,100,120,150};
pool.start(4);
std::vector<int> results;
TestTimer timer("4-sized-TP with Callback",0);
for(auto it=popList.begin(); it!=popList.end(); ++it) {
int id = *it;
auto work = [&,id]() {
int dup{0};
for(int i{0}; i < 100000 ; i++) {
auto list = generateNumbers(id);
if(hasDuplicates(list)) ++dup;
{
std::lock_guard<std::mutex> guard(m);
results.push_back(dup);
}
}
incrementCountAndNotify();
};
pool.add(work);
}
waitForNotificationOrFailOnTimeout(12);
}
结果还是出乎我的意料
按顺序花费的时间 = 37555.7ms
4-sized-TP with Future Time taken = 62544.8ms
4-sized-TP with Callback Time = 62563.6ms
完整代码和测试在 https://github.com/spakai/threadpool_future 中
最佳答案
你选择的生日悖论问题对 cpu 来说也不是一个具有挑战性的任务。但是要理解您看到的问题,我们首先必须对代码进行一些更改。
我们想测量算法完成所需的时间。内存分配是昂贵的,应该避免在程序中经常重复的部分。创建 vector 或增加它们的大小总是会触发内存分配。创建集合也是如此。为了删除 momory 分配,我将您的代码修改为如下所示:
#include "gmock/gmock.h"
#include <chrono>
#include <condition_variable>
#include <mutex>
#include <random>
#include <set>
#include <vector>
#include "ThreadPool.h"
#include "TestTimer.h"
const unsigned int runs = 100000;
using namespace testing;
class ThreadPoolTest : public Test {
public:
ThreadPool pool;
std::condition_variable wasExecuted;
std::mutex m;
std::mutex n;
std::vector<std::shared_ptr<std::thread>> threads;
std::vector<int> popList = {10,11,12,23};
unsigned int count{0};
void incrementCountAndNotify() {
{
std::unique_lock<std::mutex> lock(m);
++count;
}
wasExecuted.notify_all();
}
void waitForNotificationOrFailOnTimeout(unsigned expectedCount, int milliseconds=80000) {
std::unique_lock<std::mutex> lock(m);
ASSERT_THAT(wasExecuted.wait_for(lock, std::chrono::milliseconds(milliseconds), [&] { return count == expectedCount; }), Eq(true));
}
bool hasDuplicates(const std::vector<int> & birthdays) {
//This way to check for duplicates is very expensive, since it allocates new memory and copies all values around
//std::set<int> uniqueBirthdays(birthdays.begin(), birthdays.end());
//return (uniqueBirthdays.size() != birthdays.size());
for(unsigned int i = 0; i < birthdays.size(); i++) {
for(unsigned int j = i+1; j < birthdays.size(); j++) {
if(birthdays[i]==birthdays[j]) return true;
}
}
return false;
}
//I added the parameter list, to avoid the allocation of new memory
//The list will also have the needed size, so that we dont need to it here
std::vector<int> generateNumbers(std::vector<int>& list) {
//It is not exactly specified how the random_device works, it may read from /dev/random, which can not be done in parallel
//To make the measurements compareable over multiple machiens i removed this code
//std::random_device rd;
std::default_random_engine dre(0);
std::uniform_int_distribution<int> di(0,365);
int counter = 0;
for(int& i : list) {
i = di(dre);
}
return list;
}
void TearDown() override {
for (auto& t: threads) t->join();
}
};
TEST_F(ThreadPoolTest,BirthdayParadoxInSequenceTimingTest) {
std::vector<int> results;
TestTimer timer("Birthday Paradox :: In Sequence",0);
for(auto it=popList.begin(); it!=popList.end(); ++it) {
std::cout << "TID " << std::this_thread::get_id() << std::endl;
int id = *it;
int dup{0};
std::vector<int> list(id); //Allocate memory in the right size only once for all 100000 runs
for(int i{0}; i < runs ; i++) {
generateNumbers(list);
if(hasDuplicates(list)) ++dup;
}
results.push_back(dup); //This push_back is ok, since it is only called 4 times in total
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i);
}
}
TEST_F(ThreadPoolTest,BirthdayParadoxTPWithFutureTimingTest) {
pool.start(4);
std::vector<std::future<int>> results;
TestTimer timer("4-sized-TP with Future",0);
for(auto it=popList.begin(); it!=popList.end(); ++it) {
int id = *it;
auto work = [&](int pop) {
std::cout << "TID " << std::this_thread::get_id() << std::endl;
int dup{0};
std::vector<int> list(pop); //Same as above
for(int i{0}; i < runs ; i++) {
generateNumbers(list);
if(hasDuplicates(list)) ++dup;
}
return dup;
};
results.push_back(pool.submit(work,id));
}
for(unsigned int i = 0; i< results.size(); i++) {
results.at(i).get();
}
}
TEST_F(ThreadPoolTest,BirthdayParadoxTPWithCallBackTimingTest) {
pool.start(4);
std::vector<int> results;
TestTimer timer("4-sized-TP with Callback",0);
for(auto it=popList.begin(); it!=popList.end(); ++it) {
int id = *it;
auto work = [&,id]() {
std::cout << "TID " << std::this_thread::get_id() << std::endl;
int dup{0};
std::vector<int> list(id); //Same here too
for(int i{0}; i < runs ; i++) {
generateNumbers(list);
if(hasDuplicates(list)) ++dup;
{
std::lock_guard<std::mutex> guard(n);
results.push_back(dup);
}
}
incrementCountAndNotify();
};
pool.add(work);
}
waitForNotificationOrFailOnTimeout(4);
}
现在,我们的内存管理正确了,我们可以开始推理运行时了。我使用 2 个内核和超线程运行代码,因此如果我们使用多线程,我们期望加速为 2 或更高。让我们看看结果:
Birthday Paradox :: In Sequence Time taken = 680.96ms
4-sized-TP with Future Time taken = 1838.28ms
4-sized-TP with Callback Time taken = 1861.07ms
如果我将线程池中的线程数量限制为一个,那么所有版本的运行时间几乎相同。
我们看到这种不直观行为的原因是,问题是内存限制的。速度下降的原因在于检查重复项。
for(unsigned int i = 0; i < birthdays.size(); i++) {
for(unsigned int j = i+1; j < birthdays.size(); j++) {
if(birthdays[i]==birthdays[j]) return true;
}
}
生日的访问在内存中很好地对齐。如果有多个线程在运行,则算法不会提高速度,因为所有线程都只是在等待值。更糟糕的是,不同的线程正在从不同的位置读取,因此,它们可能会丢弃可能被其他线程使用的缓存行。这就是性能下降的原因。
关于c++ - 线程池 : single thread vs callback tp vs future tp 的计时测试,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/43860261/
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