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

Matrix multiplication is expensive O(n^3) operations! But what if we could verify the result without doing the full computation? I implemented Freivalds' algorithm in C to probabilistically verify ...

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

Abstract: Resistive RAM (RRAM) technology has emerged as a viable candidate for artificial intelligence and machine learning applications due to its matrix ...

Abstract: Digital Signal Processors (DSPs) rely on VLIW and SIMD architectures to provide significant advantages in real-time, low-power computation. The efficient implementation of matrix LU ...

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.