使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/mohitmishra786/low-level-dev-skills --skill mpi开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
MPI
Purpose
Guide agents through MPI (Message Passing Interface) programming: point-to-point and collective communication, non-blocking operations, subcommunicators, MPI+OpenMP hybrid patterns, process launching with mpirun, debugging techniques, MPI-IO, and common performance issues.
When to Use
- Parallelizing across multiple nodes or sockets
- Implementing distributed algorithms (matrix decompose, FFT)
- Combining MPI process parallelism with OpenMP thread parallelism
- Running HPC jobs with Slurm/PBS +
mpirun - Debugging deadlocks and message mismatches
- Parallel file I/O with MPI-IO
Workflow
1. Minimal MPI program
c
#include <mpi.h>
#include <stdio.h>
int main(int argc, char **argv) {
MPI_Init(&argc, &argv);
int rank, size;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
printf("Hello from rank %d of %d\n", rank, size);
MPI_Finalize();
return 0;
}bash
mpicc -o hello hello.c
mpirun -np 4 ./hello
# or
mpiexec -n 4 ./hello2. Point-to-point
c
if (rank == 0) {
int data = 42;
MPI_Send(&data, 1, MPI_INT, 1, 0, MPI_COMM_WORLD);
} else if (rank == 1) {
int recv;
MPI_Recv(&recv, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
printf("rank 1 got %d\n", recv);
}Tagged messages: match tag and source for MPI_Recv.
3. Collectives
c
int local = rank + 1;
int global_sum;
MPI_Allreduce(&local, &global_sum, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD);
// Broadcast
if (rank == 0) data = 100;
MPI_Bcast(&data, 1, MPI_INT, 0, MPI_COMM_WORLD);
// Scatter/Gather
MPI_Scatter(sendbuf, sendcount, MPI_INT, recvbuf, recvcount, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Gather(sendbuf, sendcount, MPI_INT, recvbuf, recvcount, MPI_INT, 0, MPI_COMM_WORLD);| Collective | Purpose |
|---|---|
MPI_Bcast | One-to-all |
MPI_Scatter | Distribute chunks |
MPI_Gather | Collect chunks |
MPI_Allreduce | Reduce + broadcast result |
MPI_Barrier | Synchronization |
MPI_Alltoall | All-to-all exchange |
4. Non-blocking communication
c
MPI_Request req;
MPI_Isend(buf, count, MPI_INT, dest, tag, MPI_COMM_WORLD, &req);
// overlap computation here
do_local_work();
MPI_Wait(&req, MPI_STATUS_IGNORE);
// Multiple requests
MPI_Request reqs[2];
MPI_Irecv(buf0, n, MPI_INT, 0, 0, comm, &reqs[0]);
MPI_Irecv(buf1, n, MPI_INT, 1, 0, comm, &reqs[1]);
MPI_Waitall(2, reqs, MPI_STATUSES_IGNORE);Overlap communication with computation to hide latency.
5. Subcommunicators
c
int color = rank / 4; // groups of 4
MPI_Comm subcomm;
MPI_Comm_split(MPI_COMM_WORLD, color, rank, &subcomm);
int subrank, subsize;
MPI_Comm_rank(subcomm, &subrank);
MPI_Comm_size(subcomm, &subsize);
MPI_Comm_free(&subcomm);6. MPI + OpenMP hybrid
c
#pragma omp parallel
{
int tid = omp_get_thread_num();
// thread-local work on rank's data partition
}
MPI_Barrier(MPI_COMM_WORLD);
MPI_Allreduce(...);bash
export OMP_NUM_THREADS=4
mpirun -np 8 --bind-to core ./hybrid_app
# 8 ranks × 4 threads = 32 coresBind ranks to sockets with --map-by ppr:2:socket.
7. Launching with hostfile
bash
# hostfile:
# node0 slots=4
# node1 slots=4
mpirun -np 8 --hostfile hosts.txt ./app
# Slurm integration
srun -n 64 ./app
# or
mpirun -np $SLURM_NTASKS ./appbash
# Debug: tag output by rank
mpirun -np 4 --tag-output ./app
# Sequential debug (one rank at a time)
mpirun -np 4 -gdb ./app8. MPI-IO
c
#include <mpi.h>
MPI_File fh;
MPI_File_open(MPI_COMM_WORLD, "output.dat",
MPI_MODE_CREATE | MPI_MODE_WRONLY, MPI_INFO_NULL, &fh);
MPI_Offset offset = rank * chunk_size;
MPI_File_write_at(fh, offset, buf, count, MPI_DOUBLE, MPI_STATUS_IGNORE);
MPI_File_close(&fh);Collective I/O for better performance:
c
MPI_File_write_at_all(fh, offset, buf, count, MPI_DOUBLE, MPI_STATUS_IGNORE);9. Performance issues
Common bottlenecks
├── Load imbalance → dynamic scheduling (OpenMP) or redistribute MPI chunks
├── Serialization at rank 0 → tree-based reduce, parallel I/O
├── Excessive sync → replace Barrier with point-to-point where possible
├── Small messages → aggregate; use MPI_Pack or larger blocks
└── Alltoall on large process counts → consider MPI neighborhood collectivesbash
# MPI profiling
mpiP # lightweight profiler
# or IPM, TAU MPI wrappersCommon Problems
| Symptom | Cause | Fix |
|---|---|---|
| Hang at MPI_Recv | Tag/source mismatch | Check Send/Recv pairing; use MPI_ANY_TAG debug |
| Deadlock | Circular wait | Reorder comm pattern; use non-blocking |
| Wrong result in Allreduce | Wrong datatype/count | Verify MPIINT vs MPIDOUBLE |
| Poor scaling | Rank 0 bottleneck | Distribute I/O and aggregation |
MPI_ERR_TRUNCATE | Receive buffer too small | Match send/recv counts |
| Hybrid oversubscription | Too many threads×ranks | OMP_NUM_THREADS = cores/ranks |
Related Skills
skills/hpc/openmp— thread-level parallelism within MPI ranksskills/hpc/rdma-verbs— low-latency interconnect under MPIskills/allocators/numa-programming— bind ranks to NUMA nodesskills/profilers/linux-perf— profile MPI rank hotspotsskills/debuggers/gdb— debug individual MPI processesskills/compilers/gcc— MPI compiler wrapper flags
