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Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication
MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...
NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...
This project is intended for research purposes only. Use it at your own risk and discretion. Triton is a language and compiler for writing highly efficient ML primitives, one of the most common ...
Abstract: Sparse General Matrix-Matrix Multiplication (SpGEMM) is a core operation in high-performance computing applications such as algebraic multigrid solvers, machine learning, and graph ...
This repository contains the artifact for the SC '25 paper submission "KAMI: Communication-Avoiding General Matrix Multiplication within a Single GPU." The NVIDIA GH200 is installed with Ubuntu 22.04 ...
Abstract: Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power overhead, significantly ...
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