#include <iostream>
#include <cmath>
#include <chrono>
#include <thread>
#include <vector>
#include <gmpxx.h>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <
mutex>
#include <atomic>
#include <sys/sysinfo.h>
#include <sys/resource.h>
#include <fstream>
#include <unistd.h>
#include <nvml.h> // NVIDIA GPU监控
std::
mutex output_
mutex;
std::atomic<bool> stop_monitor(false);
mpf_class pi_value(0, 0); // 高精度π值
std::atomic<int> iteration_count(0);
// 获取CPU使用率
double get_cpu_usage() {
static uint64_t last_total = 0, last_idle = 0;
std::ifstream stat_file("/proc/stat");
std::string line;
std::getline(stat_file, line);
std::istringstream iss(line);
std::string cpu;
uint64_t user, nice, system, idle, iowait, irq, softirq, steal;
iss >> cpu >> user >> nice >> system >> idle >> iowait >> irq >> softirq >> steal;
uint64_t total = user + nice + system + irq + softirq + steal;
uint64_t idle_time = idle + iowait;
double usage = 0.0;
if (last_total != 0) {
uint64_t total_diff = total - last_total;
uint64_t idle_diff = idle_time - last_idle;
usage = 100.0 * (1.0 - static_cast<double>(idle_diff) / total_diff);
}
last_total = total;
last_idle = idle_time;
return usage;
}
// 获取内存使用率
double get_memory_usage() {
struct sysinfo mem_info;
sysinfo(&mem_info);
double total = mem_info.totalram;
double free = mem_info.freeram;
return 100.0 * (1.0 - free / total);
}
// 获取GPU使用率 (NVIDIA)
double get_gpu_usage() {
nvmlReturn_t result;
nvmlDevice_t device;
unsigned int utilization;
result = nvmlInit();
if (result != NVML_SUCCESS) return -1.0;
result = nvmlDeviceGetHandleByIndex(0, &device);
if (result != NVML_SUCCESS) {
nvmlShutdown();
return -1.0;
}
result = nvmlDeviceGetUtilizationRates(device, &utilization);
if (result != NVML_SUCCESS) {
nvmlShutdown();
return -1.0;
}
nvmlShutdown();
return static_cast<double>(utilization.gpu);
}
// 资源监控线程函数
void monitor_resources() {
while (!stop_monitor) {
double cpu = get_cpu_usage();
double mem = get_memory_usage();
double gpu = get_gpu_usage();
{
std::lock_guard<std::
mutex> lock(output_
mutex);
std::cout << "\033[2K"; // 清除当前行
std::cout << "\r[资源监控] CPU: " << std::fixed << std::setprecision(1) << cpu << "% | "
<< "内存: " << mem << "% | "
<< "GPU: " << (gpu >= 0 ? std::to_string(static_cast<int>(gpu)) + "%" : "N/A");
std::cout.flush();
}
std::this_thread::sleep_for(std::chrono::seconds(1));
}
}
// Chudno
vsky算法计算π
mpf_class chudno
vsky_pi(unsigned int precision, unsigned int iterations) {
mpf_set_default_prec(precision);
mpf_class pi(0, precision);
mpf_class sum(0, precision);
mpf_class k1(13591409, precision);
mpf_class k2(545140134, precision);
mpf_class k3(-262537412640768000, precision);
mpf_class k4(426880, precision);
mpf_class k5(10005, precision);
mpf_class sqrt_c(0, precision);
k5 = sqrt(k5);
sqrt_c = k4 * k5;
mpz_class a(1);
mpz_class b(1);
mpz_class a_prime(1);
mpz_class b_prime(1);
for (unsigned int k = 0; k < iterations; k++) {
mpf_class numerator(0, precision);
mpf_class denominator(0, precision);
if (k == 0) {
a = 1;
b = 1;
} else {
// 使用二进制分裂法优化计算
a_prime = (6*k-5)*(2*k-1)*(6*k-1);
b_prime = k*k*k * k3 / 24;
a *= a_prime;
b *= b_prime;
}
numerator = k1 + k2 * k;
denominator = a * b;
sum += numerator / denominator;
iteration_count = k + 1;
// 每100次迭代显示进度
if (k % 100 == 0) {
pi = sqrt_c / sum;
std::lock_guard<std::
mutex> lock(output_
mutex);
std::cout << "\n[计算进度] 迭代: " << k << "/" << iterations
<< " | 当前π值: " << std::setprecision(15) << pi << std::endl;
}
}
pi = sqrt_c / sum;
return pi;
}
int main() {
const unsigned int precision = 100000; // 二进制精度位
const unsigned int iterations = 1000; // 迭代次数
std::cout << "开始计算π值 (Chudno
vsky算法)..." << std::endl;
std::cout << "精度: " << precision << "位 | 最大迭代: " << iterations << std::endl;
// 启动资源监控线程
std::thread monitor_thread(monitor_resources);
// 记录开始
时间
auto start_time = std::chrono::high_resolution_clock::now();
// 计算π值
pi_value = chudno
vsky_pi(precision, iterations);
// 记录结束
时间
auto end_time = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
// 停止监控线程
stop_monitor = true;
monitor_thread.join();
// 输出最终结果
std::cout << "\n\n计算完成!" << std::endl;
std::cout << "耗
时: " << duration.count() / 1000.0 << " 秒" << std::endl;
std::cout << "最终π值(前50位): " << std::setprecision(50) << pi_value << std::endl;
return 0;
}
此代码
报错，修复错误
报错信息：default.cpp:6:10: fatal error: 'gmpxx.h' file not found
#include <gmpxx.h>
^~~~~~~~~
1 error generated.
输出完整代码
本文指导读者如何解决gcc编译器中缺失mutex库的问题，推荐下载x86_64-posix-seh版本，以确保编译过程中正确包含所需文件。
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