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 ...

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: Distributed computations, such as distributed matrix multiplication, can be vulnerable to significant security issues, notably Byzantine attacks. These attacks may target either worker nodes ...

“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 ...

These are the numbers I got for multiplying two identity matrices of the size shown on the x-axis (e.g. 200x200) In the following discussion, @Kadeanon mentioned: From the implementation in MathNet, ...

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

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 ...

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 ...