In particular, we extend the DBCSR sparse matrix library, which is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. The library is ...

The evolution of data science and machine learning has increased the applicability of the sparse matrix multiplication (SPGEMM) kernel. Unlike more well-known operations such as the SPMV, in the ...

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, ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

An Efficient Block-Sparse MoE Framework This repository contains the source code and experimental results for MiSTer, a novel framework for accelerating Mixture-of-Experts (MoE) models using a ...

SparseP software package provides 25 SpMV kernels for real PIM systems supporting the four most widely used compressed matrix formats, and a wide range of data types. Our extensive evaluation provides ...

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing. However, ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...