High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning.

In the past decade, however, the parallel processing of GPUs was also found to more efficiently run the matrix multiplication algorithms needed to power AI models. AI development has two key phases.

This SpMV-based technique involves only one sparse matrix and can utilize sparse computation efficiently, so as to greatly reduce memory costs ascribed to the matrix exponential. Moreover, the ...

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library Important This project is intended for research purposes only. Use it at your own risk and discretion. Triton is a ...

In 1960, Andrey Kolmogorov posed a seemingly impossible challenge at a seminar at Moscow State University: could there be a ...

In 1960, a 23-year-old Soviet mathematician named Anatoly Karatsuba stunned the world of mathematics with a revolutionary ...

Would you believe that a growing number of physicists and technologists, including Elon Musk maintain that we are living in a ...

As a key operation in contemporary cryptosystems, modular multiplication occupies non-negligible latency and area. We first show optimizations of the k-term Karatsuba algorithm for AB/rk and ABmodrk ...

This project implements a high-speed matrix-matrix multiplication module in C/C++, optimized with multi-threading, SIMD, and cache miss minimization. It supports large, configurable matrix sizes, ...

A matrix multiplication algorithm was applied to integrate scRNA-seq data of each sample with drug–gene interactions to generate the target-based enrichment profiles of compounds as input for the ...