Abstract: Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to ...

High-performance Triton-based GPU kernels for accelerating core deep learning operations, from matrix multiplication to convolutions and activation functions. Modern deep learning frameworks rely on ...

This project provides a Python interface to GPU-accelerated matrix operations implemented in Rust using CUDA. It demonstrates significant performance improvements by leveraging NVIDIA GPUs through ...

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

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

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 ...