How to use MPI to implement distributed multi-threading in C++?

The method to use MPI to implement distributed multi-threading is as follows: Specify the multi-threading level: When initializing the MPI environment, use MPI_Init_thread() to specify the thread level (such as MPI_THREAD_MULTIPLE). Create threads: Use the standard std::thread mechanism to create threads, but use MPI thread-safe functions for MPI communication. Distribution tasks: Distribute data to different MPI processes and threads for parallel computation.

How to use MPI to implement distributed multi-threading in C++

Introduction

MPI (Message Passing Interface) is a widely used programming model for writing distributed parallel programs. It allows programmers to use message passing mechanisms to execute code in parallel on multiple computers, enabling high-performance computing. In addition to distributed parallelism, MPI also supports multi-threaded programming, which can further improve code efficiency. This article will introduce how to use MPI to implement distributed multi-threading in C++, and provide practical cases for demonstration.

MPI Multithreaded Programming

MPI_THREAD_* Options

The MPI specification defines the following options to specify the multithreading level of a program :

• MPI_THREAD_SINGLE: The program will use only one thread.
• MPI_THREAD_FUNNELED: All MPI calls of the program will be serialized, allowing only one thread to execute MPI calls at the same time.
• MPI_THREAD_SERIALIZED: The program's MPI calls will be serialized and can only be made by the main thread.
• MPI_THREAD_MULTIPLE: The program can make MPI calls in parallel and can use multiple threads.

Initialize MPI environment

To use multi-threading in MPI programs, you need to specify the thread level when initializing the MPI environment. This can be done with the following code:

int provided;
MPI_Init_thread(&argc, &argv, MPI_THREAD_MULTIPLE, &provided);

Parameters provided Indicates the level of multithreading provided by the MPI library. If provided is equal to MPI_THREAD_MULTIPLE, it indicates that the MPI library supports multi-threaded programming.

Creating threads

Using std::thread The standard method of creating threads is also available in MPI programs, but requires additional considerations. To ensure that MPI calls are synchronized correctly across threads, MPI thread-safe functions are required for MPI communication.

The following is an example of creating a thread:

std::thread thread([&]() {
  // 在新线程中执行 MPI 调用
});

Practical case

Now let’s look at a practical case to demonstrate how to use MPI multi-thread acceleration Matrix multiplication calculation.

Matrix multiplication

Given two matrices A and B, where the size of A is m x n, B has size n x p, and the result of matrix multiplication C = A * B has size C m x p.

MPI Parallelization

Using MPI to parallelize matrix multiplication calculations, you can assign the rows of the

A matrix to different MPI processes and let each A process computes the product of a local submatrix and the B matrix.

Multi-thread acceleration

In each MPI process, multi-threading can be used to further accelerate calculations. Assign the columns of the

B matrix to different threads, making each thread responsible for computing the product of the local submatrix and a column of the B matrix.

// MPI 主程序
int main(int argc, char** argv) {
  // 初始化 MPI 环境
  int provided;
  MPI_Init_thread(&argc, &argv, MPI_THREAD_MULTIPLE, &provided);

  // 创建 MPI 通信器
  MPI_Comm comm = MPI_COMM_WORLD;
  int rank, size;
  MPI_Comm_rank(comm, &rank);
  MPI_Comm_size(comm, &size);

  // 分配矩阵行并广播矩阵 B
  ...

  // 创建线程池
  std::vector<std::thread> threads;

  // 计算局部子矩阵乘积
  for (int i = 0; i < columns_per_thread; i++) {
    threads.push_back(std::thread([&, i]() {
      ...
    }));
  }

  // 等待所有线程完成
  for (auto& thread : threads) {
    thread.join();
  }

  // 汇总局部结果并输出 C 矩阵
  ...

  // 结束 MPI 环境
  MPI_Finalize();

  return 0;
}

conclusion

By using MPI multithreading, you can combine the advantages of distributed parallelism and multithreaded programming to significantly improve the performance of C++ programs. The above practical case shows how to apply MPI multithreading to matrix multiplication calculations to parallelize and accelerate the calculation process.

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