mpich
A high performance implementation of MPI
Decision gist · record as of 2026-08-14
Yes, if you are building or running parallel HPC applications on a cluster and need a widely-adopted, vendor-supported MPI implementation. No, if you are looking for a Python library for parallel computing—this is a system runtime, not a Python package. Yes-with-conditions if you are unfamiliar with MPI or HPC: expect to configure and install to a shared filesystem, understand process management, and have appropriate compilers available.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a C99-compliant C compiler; optional Fortran and C++ compilers if building Fortran or C++ applications.
- Installation directory must be at the same path on all nodes in a cluster (typically on shared NFS).
- Medium install friction due to platform-specific wheels (macOS arm64/x86_64, Linux aarch64/x86_64) and no runtime Python dependencies, but requires system-level C compiler and optional Fortran/C++ compilers depending on use case.
License · maintenance · safety
LicenseRef-MPICH (unclear) — License treatment is unclear—the package uses a custom LicenseRef-MPICH identifier with no SPDX mapping. Review the project's actual license terms before adopting in proprietary or restricted-distribution contexts.
last release 2026-07-16 (29 days) · last repo commit 2026-08-10 · 685 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 91,121 downloads/mo, #13,539 on PyPI
Alternatives
Verify before relying
# After installation, add to PATH:
export PATH=/path/to/mpich-install/bin:$PATH
# Compile an MPI program:
mpicc -o myapp myapp.c
# Run on local machine with n processes:
mpiexec -n 4 ./myapp- Whether the PyPI wheel includes pre-built MPICH binaries or requires local compilation.
- Python version requirements or compatibility constraints not specified in the fact sheet.
- Whether the package works with Windows or only Unix-like systems.
What it is and what it does
MPICH is a production-grade implementation of the Message Passing Interface standard, widely deployed on HPC systems and supported by vendors including Intel and HPE. It provides the mpicc compiler wrapper, mpiexec process manager, and core MPI runtime needed to build and run parallel applications across single or multiple nodes. The package comes with the Hydra process manager by default and supports multiple network modules (OFI, UCX) for different interconnect hardware.
Typical use involves configuring MPICH with a chosen device and network module, installing it to a shared filesystem path visible on all compute nodes, then using mpicc to compile parallel C/C++/Fortran code and mpiexec to launch jobs across the cluster. It is not a Python library in the traditional sense—it is a system-level MPI runtime that Python applications can call into via bindings.
Use it for
- Compile and run parallel C/C++/Fortran applications on HPC clusters using standard MPI calls.
- Set up a multi-node compute environment where applications communicate via message passing.
- Deploy on Top 500 HPC systems or vendor-supported clusters (Intel, HPE, ParTec infrastructure).
- Develop and test parallel algorithms that require inter-process communication across distributed nodes.
- Integrate with existing HPC workflows that expect mpicc, mpiexec, and standard MPI headers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or running parallel HPC applications on a cluster and need a widely-adopted, vendor-supported MPI implementation.
No, if you are looking for a Python library for parallel computing—this is a system runtime, not a Python package. Yes-with-conditions if you are unfamiliar with MPI or HPC: expect to configure and install to a shared filesystem, understand process management, and have appropriate compilers available.
Install
mpich on PyPI
Before you install
Medium install friction due to platform-specific wheels (macOS arm64/x86_64, Linux aarch64/x86_64) and no runtime Python dependencies, but requires system-level C compiler and optional Fortran/C++ compilers depending on use case.
Requires a C99-compliant C compiler; optional Fortran and C++ compilers if building Fortran or C++ applications. Installation directory must be at the same path on all nodes in a cluster (typically on shared NFS).
License in practice
License treatment is unclear—the package uses a custom LicenseRef-MPICH identifier with no SPDX mapping. Review the project's actual license terms before adopting in proprietary or restricted-distribution contexts.
Quickstart
# After installation, add to PATH:
export PATH=/path/to/mpich-install/bin:$PATH
# Compile an MPI program:
mpicc -o myapp myapp.c
# Run on local machine with n processes:
mpiexec -n 4 ./myappVerify before relying
- Whether the PyPI wheel includes pre-built MPICH binaries or requires local compilation.
- Python version requirements or compatibility constraints not specified in the fact sheet.
- Whether the package works with Windows or only Unix-like systems.
Package facts
| License | LicenseRef-MPICH unclear |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 91,121 / month, #13,539 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: mpich-5.0.1.post1-py3-none-macosx_11_0_arm64.whl; mpich-5.0.1.post1-py3-none-macosx_11_0_x86_64.whl; mpich-5.0.1.post1-py3-none-manylinux_2_28_aarch64.whl; mpich-5.0.1.post1-py3-none-manylinux_2_28_x86_64.whl
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See also impi-rt · mpi4py · openmpi · pytest-mpi · intel-openmp · distributed-ucxx-cu12 · distributed · oneccl-devel · nvidia-nvshmem-cu13 · carolina