IEEE

MH-SpGEMM: Efficient Sparse General Matrix-Matrix Multiplication on Modern GPUs via Masking and Hashing Cooperative Optimization

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 ...
IEEE

Improved Modular Multiplication Algorithms Using Solely IEEE 754 Binary Floating-Point Operations

Abstract: In this paper, we propose three modular multiplication algorithms that use only the IEEE 754 binary floating-point operations. Several previous studies have used floating-point operations to ...
GitHub

Implications of Language Choice for Algorithmic Efficiency in Low-Power Computing

This repository accompanies the research paper "Implications of Language Choice for Algorithmic Efficiency in Low-Power Computing." The study investigates how the choice of programming ...
GitHub

KAMI: Communication-Avoiding General Matrix Multiplication within a Single GPU

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 ...