C++与FastMCP高效并行实战

基于C++和FastMCP

以下是一些基于C++和FastMCP（假设指高性能计算或多核处理相关库）的实用示例，涵盖不同应用场景。由于FastMCP并非标准库，以下示例假设其功能类似于多线程、并行计算或特定领域的加速库。

并行数组求和

使用多线程对大型数组进行分块求和：

#include #include #include void parallel_sum(const std::vector& data, int start, int end, int& result) { result = std::accumulate(data.begin() + start, data.begin() + end, 0);}int main() { std::vector data(1000000, 1); // 1百万个1 int num_threads = 4; std::vector threads; std::vector partial_results(num_threads, 0); for (int i = 0; i < num_threads; ++i) { int start = i * data.size() / num_threads; int end = (i + 1) * data.size() / num_threads; threads.emplace_back(parallel_sum, std::ref(data), start, end, std::ref(partial_results[i])); } for (auto& t : threads) t.join(); int total = std::accumulate(partial_results.begin(), partial_results.end(), 0); return 0;}

矩阵乘法优化

分块矩阵乘法提升缓存利用率：

const int BLOCK_SIZE = 32;void block_matrix_multiply(float* A, float* B, float* C, int N) { for (int i = 0; i < N; i += BLOCK_SIZE) for (int j = 0; j < N; j += BLOCK_SIZE) for (int k = 0; k < N; k += BLOCK_SIZE) for (int ii = i; ii < i + BLOCK_SIZE; ++ii)  for (int jj = j; jj < j + BLOCK_SIZE; ++jj) for (int kk = k; kk < k + BLOCK_SIZE; ++kk) C[ii*N + jj] += A[ii*N + kk] * B[kk*N + jj];}

快速排序并行化

使用C++17的并行算法：

#include #include #include int main() { std::vector data = {...}; std::sort(std::execution::par, data.begin(), data.end()); return 0;}

蒙特卡洛模拟

并行计算Pi值：

#include #include #include double monte_carlo_pi(int samples) { std::mt19937 gen(std::random_device{}()); std::uniform_real_distribution dis(0.0, 1.0); int hits = 0; for (int i = 0; i < samples; ++i) { double x = dis(gen), y = dis(gen); if (x*x + y*y <= 1) hits++; } return 4.0 * hits / samples;}int main() { auto f1 = std::async(std::launch::async, monte_carlo_pi, 1000000); auto f2 = std::async(std::launch::async, monte_carlo_pi, 1000000); std::cout << (f1.get() + f2.get()) / 2;}

图像处理卷积

SIMD优化卷积运算：

#include  // AVX指令集void convolve_avx(float* input, float* output, float* kernel, int width, int height) { for (int y = 1; y < height - 1; ++y) { for (int x = 1; x < width - 1; x += 8) { // 每次处理8个像素 __m256 sum = _mm256_setzero_ps(); for (int ky = -1; ky <= 1; ++ky) { for (int kx = -1; kx <= 1; ++kx) {  __m256 pix = _mm256_loadu_ps(&input[(y+ky)*width + x+kx]);  __m256 kern = _mm256_set1_ps(kernel[(ky+1)*3 + (kx+1)]);  sum = _mm256_fmadd_ps(pix, kern, sum); } } _mm256_storeu_ps(&output[y*width + x], sum); } }}

哈希表并发访问

使用TBB库的并发哈希表：

#include tbb::concurrent_hash_map table;void insert_data(int key, const std::string& value) { tbb::concurrent_hash_map::accessor acc; table.insert(acc, key); acc->second = value;}

数值积分

并行梯形法积分：

#include #include double integrate(double a, double b, int n, double (*f)(double)) { double h = (b - a) / n; double sum = 0.5 * (f(a) + f(b)); for (int i = 1; i < n; ++i) sum += f(a + i * h); return sum * h;}int main() { auto f1 = std::async(integrate, 0, 1, 500000, std::sin); auto f2 = std::async(int