# NVIDIA/MatX

An efficient C++20 GPU numerical computing library with Python-like syntax

Repository: https://github.com/NVIDIA/MatX
Canonical: https://ross.abutalabs.com/products/matx
Homepage: https://nvidia.github.io/MatX
Language: C++
License: BSD-3-Clause
License Family: permissive
Topics: cuda, gpu, gpgpu, hpc, gpu-computing
Last push: 2026-08-26T18:11:40+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 72, longevity 100
- inputs: {"age_days": 1814, "days_push": 7, "days_rel": 26, "gap_med": 102.0, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1444, forks 126 (observed 2026-08-28T04:04:44.983973+00:00)

## What it is
MatX is a C++20 header-only numerical computing library providing NumPy-like tensor expressions for NVIDIA GPUs and multithreaded CPUs. It offers lazy expression evaluation, runtime kernel fusion, and easy frontends to CUDA and CPU libraries like cuBLAS, FFTW, and OpenBLAS.

## Use cases
- write NumPy-style array code in C++ on GPUs
- fuse numerical pipelines to avoid kernel launches and memory traffic
- perform linear algebra, FFTs, convolutions, and reductions on tensors
- run the same tensor code on GPU or multithreaded CPU
- prototype GPU kernels without writing raw CUDA
- accelerate signal processing and scientific computing workloads

## When to choose
- you need NumPy-like productivity with native C++ performance on NVIDIA GPUs
- you want a header-only library with no compilation step
- you want to switch between GPU and CPU executors without changing your algorithm
- you need high-performance tensor math for HPC or signal processing

## When to avoid
- you need multi-vendor GPU support beyond NVIDIA
- you primarily work in Python and don't need C++ integration
- you need a full deep-learning framework with autodiff and training loops
- your project cannot use C++20

## Facets
- artifact type: library
- maturity: active
- function: gpu-computing, math, simulation, machine-learning
- domain: gpu-computing, performance, data-science, developer-tools
- platform: cpp, windows, cross-platform
- tags: numerical-computing, tensors, numpy-like, header-only, c++20, hpc, gpgpu, lazy-evaluation, linear-algebra, fft, algorithms, cuda, gpu, linux

## Member repositories
- NVIDIA/MatX (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:44.983973+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:36:16.396658+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/MatX (fetched 2026-08-28T04:04:44.983973+00:00, sha 4d81f2719334)
  - homepage: https://nvidia.github.io/MatX (fetched 2026-08-29T11:46:11.102000+00:00, sha 723e01bad143)
- Data as of 2026-08-30T08:39:29.467469+00:00.