[参考]多线程之争：用C还是用python

多线程之争：用C还是用python

本文主要介绍不同负载下，两种不同的编码方式的多线程运行速度，作为参考。

背景

一些任务，例如下载网页图片、爆破哈希、大规模运算等任务，用单线程跑显然太慢。然而在一次跑熵计算的应用中，使用python的concurrent.further写个协程跑的还更慢了，索性换C来跑，结果快的不少。作为开发技术，在这里记录一下。

将讨论下述应用场景并给出一定的代码示例，主要是自用。

  • python：使用多线程下载telegra.ph的图片
  • C：使用WinAPI进行并行计算，并和python的等价代码进行性能比较

首先要说一说并发和并行的区别（操作系统基础）：

image-20230629095635160
  • 并发（Concurrency）：实际上是处理器调度。单个处理单元轮换调度并处理诸多问题，这个“诸多”问题可以是某一个问题的子任务。
  • 并行（Parallelism）：将一个任务拆分为多个子任务，多个子任务分配给不同的多个处理单元，通过物理处理机的硬件优势同时的处理多个任务。

因此，对于大部分任务，结合现在的计算机硬件性能，我觉得并行的需求更为明确。

对于有人问的异步和同步，这是编程概念。异步是一种编程模型，用于处理非阻塞式的任务调度和执行。

还有协程。协程的系统资源占用比thread小，对于异步开发更佳，但是对于阻塞IO来说是个头疼的问题(至少我不会)

Python的假并发——GIL

GIL只存在于CPython，即使用C语言实现的Python上。

In CPython, the global interpreter lock, or GIL, is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once. The GIL prevents race conditions and ensures thread safety.

GlobalInterpreterLock - Python Wiki

全局解释器锁

CPython中，为了安全性和同步问题，Python的实现器在所有多线程环境中，不论有多少线程，都只允许同时存在一个正在执行字节码的线程。GIL实际上是一种互斥机制，避免了内存访问问题（例如多个线程同时读写一块内存导致的混乱）和变量竞争（例如多个线程同时读写一个变量，本质上是内存竞争）。作为一种全局的互斥锁，此锁避免了大部分多线程的编程问题by killing multithreading

在CPython中，每一个Python线程执行前都需要去获得GIL锁 ，获得该锁的线程才可以执行，没有获得的只能等待 ，当具有GIL锁的线程运行完成后，其他等待的线程就会去争夺GIL锁，这就造成了，在Python中使用多线程，但同一时刻下依旧只有一个线程在运行 ，所以Python多线程其实并不是「并行」的，而是「并发」 。

虽然 Python 在某些情况下受到 GIL 的限制，但仍然可以通过多进程、异步编程和外部扩展模块等方式实现并发计算，以提高程序的性能和效率。选择适当的并发模型取决于任务的性质和要求。

当然，这个问题已经在2023年1月被提出，并作为Proposal（PEP 703 – Making the Global Interpreter Lock Optional in CPython | peps.python.org）提请到了Python的委员会。这个Proposal提议用一个开关--disable-gil来关闭GIL，让程序员自己保证线程安全。

实例

Python下载图片

首先给出一个使用Python下载网页的示例。对于互联网下载这种存取型任务，其主要的性能瓶颈在于网速和网络限制，因此对于并发和并行而言，虽然可以使用并行，但是效率提升不大。

https://gist.github.com/Holit/d89818543b7c4d5938348bfaaaec3dfb

#telegra.ph image download script
import urllib.request
from bs4 import BeautifulSoup
import re
import gzip
import sys
import os
import threading
import concurrent.futures
import requests


SavePath = ""


headers = {
    'Host':'telegra.ph',
    'Connection':'keep-alive',
    'Cache-Control':'max-age=0',
    'sec-ch-ua':'"Chromium";v="112", "Microsoft Edge";v="112", "Not:A-Brand";v="99"',
    'sec-ch-ua-mobile':'?0',
    'sec-ch-ua-platform':"Windows",
    'DNT':'1',
    #'Upgrade-Insecure-Requests':' 1',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36 Edg/112.0.1722.58',
    'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
    'Accept-Language' : 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    'Sec-Fetch-Site':'none',
    'Sec-Fetch-Mode':'navigate',
    'Sec-Fetch-User':'?1',
    'Sec-Fetch-Dest':'document',
    #'Referer':' https://www.google.com.hk/',
    'Accept-Encoding':'gzip',
    #'Accept-Language':' zh-CN,zh;q=0.9',
    #'Cookie':' country=hk',
}

def get_page_img_url(base_url):
    host = ''
    match = re.match(r"(https?://[^\s/]+)",base_url)
    if match:
        host = match.group(1)
    else:
        raise Exception('[!] Unable to find host url! - This problem shall not happen!')
    url_dict = []
    req = urllib.request.Request(base_url, headers=headers)
    res = urllib.request.urlopen(req)
    data = res.read()
    if res.headers.get('Content-Encoding') == 'gzip':
        data = gzip.decompress(data)
    soup = BeautifulSoup(data,"html.parser")
    div_elements = soup.find(name='article', attrs={'class': 'tl_article_content'})
    img_dicts = div_elements.findAll('img')
    for img_item in img_dicts:
        url = img_item.get('src')
        url_dict.append(host + url)
    return url_dict

max_threads = 16
thread_lock = threading.BoundedSemaphore(value=max_threads)
def download_image(url, filename):
    try:
        response = requests.get(url,headers=headers)
        if response.status_code == 200:
            with open(filename, "wb") as file:
                file.write(response.content)
                print(f' [+] Successfully downloaded {filename}')
                thread_lock.release()
        #req = urllib.request.Request(url)
        #response = urllib.request.urlopen(req)
        #with open(filename, "wb") as file:
        #    file.write(response.read(response.length))   
        #    print(f' [+] Successfully downloaded {filename}')
        #    thread_lock.release()
    except Exception as e:
        print(f'[!] Exception : {url} -> {filename}')
        print(e)
def download_imgs(url_dict, path : str):
    try:
        threads = []
        for url in url_dict:
            file_name = re.search(r"/([^/]+)$", url).group(1)
            thread_lock.acquire()
            thread = threading.Thread(target=download_image, args=(url, path + file_name))
            threads.append(thread)
            thread.start()
        for thread in threads:
            thread.join()
    except:
        input()


BaseURL = sys.argv[1]
host = ''
match = re.match(r"(https?://[^\s/]+)",BaseURL)
if match:
    host = match.group(1)
    print(f'[+] Host={host}')
else:
    raise Exception('[!] Unable to find host url! - This problem shall not happen!')

print(f'[+] SavePath={SavePath}')
req = urllib.request.Request(BaseURL, headers=headers)
res = urllib.request.urlopen(req)
data = res.read()
if res.headers.get('Content-Encoding') == 'gzip':
    data = gzip.decompress(data)
soup = BeautifulSoup(data,"html.parser")
h1 = soup.find('h1')
records = []
records.append({'title' : h1.string, 'url' : BaseURL})
div_elements = soup.find(name='article', attrs={'class': 'tl_article_content'})

a_dicts = div_elements.findAll('a')
print(f'[+] Page contains {len(a_dicts)} links.')
for a_item in a_dicts:
    if(a_item.get('target') == None):
        title = a_item.string
        url = a_item.get('href')
        records.append({'title' : title, 'url' : host + url})  
print(f'[+] Fetched {len(records)} records.')

for record in records:
    title = record['title']
    url = record['url']

    print(f'[+] Downloading {title}...')
    parent_path = SavePath + title + '\\'
    if(not os.path.exists(parent_path)):
        os.mkdir(parent_path)
    img_urls = get_page_img_url(url)
    
    download_imgs(img_urls, parent_path)

这里使用了thread的方法，同样的，可以用concurrent实现download_imgs

#...
def download_imgs(url_dict, path : str):
    try:
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = []
            for url in url_dict:
                file_name = re.search(r"/([^/]+)$", url).group(1)
                future = executor.submit(download_image, url, path + file_name)
                futures.append(future)
            for future in concurrent.futures.as_completed(futures):
                result = future.result()
    except Exception as e:
        input()
#...

用协程写：

#...
async def download_imgs(url_dict, path):
    try:
        tasks = []
        async with aiohttp.ClientSession() as session:
            for url in url_dict:
                file_name = re.search(r"/([^/]+)$", url).group(1)
                task = asyncio.create_task(download_image(session, url, path + file_name))
                tasks.append(task)
            await asyncio.gather(*tasks)
    except Exception as e:
        print(e)

async def download_image(session, url, file_path):
    async with session.get(url) as response:
        with open(file_path, 'wb') as file:
            while True:
                chunk = await response.content.read(1024)
                if not chunk:
                    break
                file.write(chunk)
#...

计算熵

这是一个测试项目，从一个文件中逐字节读取\(N\)个字节，并计算前\(N\)个字节的信息熵。信息熵的计算公式如下：

还可以定义事件 X 与 Y 分别取 \(x_i\) 和\(y_i\)时的条件熵为

\[{\displaystyle \mathrm {H} (X|Y)=-\sum _{i,j}p(x_{i},y_{j})\log {\frac {p(x_{i},y_{j})}{p(y_{j})}}} \]

使用Python写一个，这里利用了concurrent.future。

from importlib.metadata import entry_points
import os 
import sys
import math
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
import concurrent.futures
import threading
import time

def calculate_entropy(string):
    # 统计字符频率
    char_count = Counter(string)
    total_chars = len(string)

    # 计算每个字符的概率
    probabilities = [char_count[char] / total_chars for char in char_count]

    # 计算信息熵
    entropy = -sum(p * math.log2(p) for p in probabilities)

    return entropy

def calculate_entropy_incremental(file_path):
    entropy_list = []

    def calculate_entropy_partial(partial_text,index):
        entropy = calculate_entropy(partial_text)

        # 使用互斥锁确保线程安全地添加到列表
        entropy_list.append(
            {
                'value': entropy,
                'index': index
            }
        )

    with open(file_path, 'r', encoding='utf-8') as file:
        text = file.read()

    chunk_size = 1  # 将文件分成32个块
    pos = 0

    with concurrent.futures.ThreadPoolExecutor(max_workers=128) as executor:
        futures = []
        while pos < len(text):
            partial_text = text[0:pos]
            future = executor.submit(calculate_entropy_partial, partial_text,pos)
            futures.append(future)
            pos = pos+1

        # 等待所有线程完成
        concurrent.futures.wait(futures)

    # 按照字节顺序排序
    #entropy_list.sort(key=lambda x: x[0])

    entropy_list = sorted(entropy_list, key=lambda x: x['index'])
    entropy_list = [item['value'] for item in entropy_list]
    return entropy_list

def plot_entropy(entropy_list):
    x = range(len(entropy_list))
    y = entropy_list

    plt.plot(x, y)
    plt.xlabel('Bytes')
    plt.ylabel('Entropy')
    plt.title('File entropy(incremental)')

    # 计算最大值和平均值
    max_entropy = max(entropy_list)
    avg_entropy = np.mean(entropy_list)
    # 绘制最大值线
    plt.axhline(max_entropy, color='red', linestyle='--', label='Max Entropy')

    # 绘制平均值线
    plt.axhline(avg_entropy, color='blue', linestyle='--', label='Average Entropy')

    # 标注熵值
    plt.text(0, max_entropy, f'{max_entropy:.2f}', ha='left', va='center', color='red')
    plt.text(0, avg_entropy, f'{avg_entropy:.2f}', ha='left', va='center', color='blue')

    plt.legend()
    plt.show()
if len(sys.argv) < 2:
    raise Exception(f'Usage: {sys.argv[0]} filepath')
if os.path.exists(sys.argv[1]) == False:
    raise Exception(f'Invalid path {sys.argv[1]}')

start_time = time.time()
entropy_list = calculate_entropy_incremental(sys.argv[1])
end_time = time.time()
# 计算耗时（毫秒）
elapsed_time = (end_time - start_time) * 1000
print("总耗时：{:.4f} 毫秒".format(elapsed_time))

plot_entropy(entropy_list)

同样的，使用C写一个，直接用WinAPI

#define _CRT_SECURE_NO_WARNINGS
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include <string.h>
#include <windows.h>
#include <stdint.h>
#include <intrin.h>
static double* entropy_list = NULL;
static long len_entropy_list = 0;
static long entropy_list_pos = 0;

static long segment_size = 0;

char* text = NULL;
#define THREAD_COUNT 32
CRITICAL_SECTION cs;
HANDLE hMutex;           // 互斥锁
HANDLE hMutexText;

double calculate_entropy(const char* string) {
	int char_count[256] = { 0 };
	int total_chars = 0;

	// 统计字符频率
	for (int i = 0; string[i] != '\0'; i++) {
		char_count[(unsigned char)string[i]]++;
		total_chars++;
	}

	// 计算每个字符的概率
	double probabilities[256];
	for (int i = 0; i < 256; i++) {
		probabilities[i] = (double)char_count[i] / total_chars;
	}

	// 计算信息熵
	double entropy = 0;
	for (int i = 0; i < 256; i++) {
		if (char_count[i] > 0) {
			double p = probabilities[i];
			entropy -= p * log2(p);
		}
	}

	return entropy;
}
DWORD WINAPI CalculateEntropyThread(LPVOID lpParam)
{
	long pos = (long)lpParam;
	for (int i = 0; i < segment_size; i++)
	{
		EnterCriticalSection(&cs);
		WaitForSingleObject(hMutexText, INFINITE);
		char* partial_text = (char*)malloc((pos + i + 1) * sizeof(char));
		memset(partial_text, 0, (pos + i + 1) * sizeof(char));
		if (text == NULL || partial_text == NULL)
		{
			throw "Unexpected null pointer: text!";
			return -4;
		}
		strncpy(partial_text, text, pos + i);
		ReleaseMutex(hMutexText);

		partial_text[pos + i] = '\0';

		WaitForSingleObject(hMutex, INFINITE);

		entropy_list[pos + i] = calculate_entropy(partial_text);

		ReleaseMutex(hMutex);
		LeaveCriticalSection(&cs);
		free(partial_text);
		InterlockedIncrement(&entropy_list_pos);
	}
	return 0;
}
double* calculate_entropy_incremental(const char* file_path) {
	FILE* file = fopen(file_path, "r");
	if (file == NULL) {
		printf("Invalid path: %s\n", file_path);
		return NULL;
	}

	long file_size = 0;
	fseek(file, 0, SEEK_END);
	file_size = ftell(file);
	rewind(file);
	text = (char*)VirtualAlloc(NULL, file_size + 1, MEM_COMMIT, PAGE_READWRITE);
	fread(text, sizeof(char), file_size, file);
	text[file_size] = '\0';

	entropy_list = (double*)malloc((file_size + 1) * sizeof(double));
	len_entropy_list = file_size + 1;
	int pos = 0;
	const int numThreads = 8;
	HANDLE hThreads[numThreads];

	segment_size = file_size / numThreads;

	// 动态生成线程
	for (int i = 0; i < numThreads; i++)
	{
		hThreads[i] = CreateThread(NULL, 0, CalculateEntropyThread, (LPVOID)(i * segment_size), 0, NULL);
		if (hThreads[i] == NULL)
		{
			printf("Failed to create thread %d\n", i);
		}
	}
	WaitForMultipleObjects(numThreads, hThreads, TRUE, INFINITE);  
	// 释放线程资源
	for (int i = 0; i < numThreads; i++)
	{
		CloseHandle(hThreads[i]);
	}
	VirtualFree(text, 0, MEM_RELEASE);
	fclose(file);
	entropy_list[file_size] = entropy_list[file_size - 1];
	return entropy_list;
}

void plot_entropy(const double* entropy_list, int size) {
	// 绘制折线图的代码，请根据需要使用合适的绘图库

	// 例如，可以使用gnuplot库进行绘图
	FILE* gnuplot = _popen("gnuplot -persist", "w");
	if (gnuplot != NULL) {
		fprintf(gnuplot, "set term wxt size 1920, 1080\n"); // 设置绘图窗口分辨率为 1024x768
		fprintf(gnuplot, "set xlabel 'Bytes'\n");
		fprintf(gnuplot, "set ylabel 'Entropy'\n");
		fprintf(gnuplot, "set title 'File entropy(incremental)'\n");
		fprintf(gnuplot, "plot '-' with lines title 'Entropy'\n");

		for (int i = 0; i < size; i++) {
			fprintf(gnuplot, "%d %f\n", i, entropy_list[i]);
		}

		fprintf(gnuplot, "e\n");
		_pclose(gnuplot);
	}
	else
	{
		printf("Unable to open gnuplot for configuration.\n\n");
		return;
	}
}

int main(int argc, char* argv[]) {
	if (argc < 2) {
		printf("Usage: %s filepath\n", argv[0]);
		return 1;
	}
	if (GetFileAttributesA(argv[1]) == INVALID_FILE_ATTRIBUTES)
	{
		printf("Invalid path: %s\n", argv[1]);
		return 1;
	}
	uint64_t start, end;
	uint64_t frequency;
	if (!QueryPerformanceFrequency((LARGE_INTEGER*)&frequency)) {
		printf("Failed to query performance frequency.\n");
		return 1;
	} 
	InitializeCriticalSection(&cs);
	start = __rdtsc();
	double* entropy_list = calculate_entropy_incremental(argv[1]);
	end = __rdtsc();
	DeleteCriticalSection(&cs);
	if (entropy_list == NULL)return -1;
	double elapsed_time = (double)(end - start) / frequency;
	printf("Total time: %.6f milliseconds\n", elapsed_time);

	if (entropy_list != NULL) {
		plot_entropy(entropy_list, len_entropy_list);
		free(entropy_list);
	}

	return 0;
}

结果是

命令提示符_230628172903

如果把Python版本的单线程按照上述代码更改为多线程（32），其性能提升如何呢？

image-20230629105100619

答：没有提升，原因是这是一个计算密集型任务，单纯的轮换调度（并发）并不能很好的完成这个任务。

结论

  • 对于计算密集任务，请用C
  • 对于Python的并发，请用concurrent
posted @ 2023-06-29 10:56  二氢茉莉酮酸甲酯  阅读(119)  评论(0)  编辑  收藏  举报