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

In this video from PASC17, Alfio Lazzaro (University of Zurich, Switzerland) presents: Increasing Efficiency of Sparse Matrix-Matrix Multiplication. “Matrix-matrix multiplication is a basic operation ...

Abstract: Sparse-matrix dense-matrix multiplication (SpMM) receives one sparse matrix and one dense matrix as two inputs, and outputs one dense matrix as a result. It plays a vital role in various ...

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 ...

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

This repository contains the source code and experimental results for MiSTer, a novel framework for accelerating Mixture-of-Experts (MoE) models using a high-performance, block-sparse matrix ...

N:M sparsity is becoming increasingly popular for its potential to deliver high model accuracy and computational efficiency for deep learning. However, the real-world benefit of N:M sparsity is ...

“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...

This project implements high-performance dense-dense, dense-sparse, and sparse-sparse matrix multiplication using C++ with configurable multi-threading, SIMD optimizations, and cache miss minimization ...