RunMat

Details

External ID

46121951

Source

HN

Company

—

Product

RunMat

Website domain

github.com

Launched

Dec. 2, 2025

Cohort

—

Upvotes

21

Upvotes percentile

0.6612595419847328

Tags

—

Fetched at

Sept. 7, 2026, 9:25 p.m.

Updated at

Sept. 7, 2026, 9:25 p.m.

Description

Hi, I’m Nabeel. In August I released RunMat as an open-source runtime for MATLAB code that was already much faster than GNU Octave on the workloads I tried. https://news.ycombinator.com/item?id=44972919Since then, I’ve taken it further with RunMat Accelerate: the runtime now automatically fuses operations and routes work between CPU and GPU. You write MATLAB-style code, and RunMat runs your computation across CPUs and GPUs for speed. No CUDA, no kernel code.Under the hood, it builds a graph of your array math, fuses long chains into a few kernels, keeps data on the GPU when that helps, and falls back to CPU JIT / BLAS for small cases.On an Apple M2 Max (32 GB), here are some current benchmarks (median of several runs):* 5M-path Monte Carlo * RunMat ≈ 0.61 s * PyTorch ≈ 1.70 s * NumPy ≈ 79.9 s → ~2.8× faster than PyTorch and ~130× faster than NumPy on this test.* 64 × 4K image preprocessing pipeline (mean/std, normalize, gain/bias, gamma, MSE) * RunMat ≈ 0.68 s * PyTorch ≈ 1.20 s * NumPy ≈ 7.0 s → ~1.8× faster than PyTorch and ~10× faster than NumPy.* 1B-point elementwise chain (sin / exp / cos / tanh mix) * RunMat ≈ 0.14 s * PyTorch ≈ 20.8 s * NumPy ≈ 11.9 s → ~140× faster than PyTorch and ~80× faster than NumPy.If you want more detail on how the fusion and CPU/GPU routing work, I wrote up a longer post here: https://runmat.org/blog/runmat-accel-intro-blogYou can run the same benchmarks yourself from the GitHub repo in the main HN link. Feedback, bug reports, and “here’s where it breaks or is slow” examples are very welcome.

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