* Program re-ordering for improved L2 cache hit rate. * Automatic performance tuning. # Motivations # Matrix multiplications are a key building block of most modern high-performance computing systems.

Description When multiplying a chain of matrices, the order of multiplication doesn't change the result (matrix multiplication is associative), but it dramatically changes the number of scalar ...

NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...

In this tutorial, we implement an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. We start by ...

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 ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

A recent paper set the fastest record for multiplying two matrices. But it also marks the end of the line for a method researchers have relied on for decades to make improvements. For computer ...