RunMat – runtime with auto CPU/GPU routing for dense math
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=44972919 Since 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…
In plain words
RunMat is an open-source runtime that executes MATLAB-style code with automatic optimization across CPUs and GPUs. Developers write standard array mathematics without CUDA or kernel code, while RunMat automatically fuses operations and routes computations to available hardware for faster execution. The runtime builds computation graphs, minimizes GPU transfers, and falls back to CPU acceleration for smaller workloads, eliminating manual hardware management from scientific computing workflows.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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=44972919 Since 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-blog You 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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