Intel and AMD have jointly announced ACE, a new x86 instruction set extension that brings dedicated AI acceleration to CPUs, ...

Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, ...

DBCSR is a library designed to efficiently perform sparse matrix-matrix multiplication, among other operations. It is MPI and OpenMP parallel and can exploit Nvidia and AMD GPUs via CUDA and HIP. To ...

Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

Abstract: Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast and ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

This repository contains the CUDA kernels for general matrix-matrix multiplication (GEMM) and the corresponding performance analysis. The correctness of the CUDA kernels is guaranteed for any matrix ...

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...

α-Calcium sulfate hemihydrate (α-CSH) is a widely used artificial bone graft material, but it suffers from rapid deterioration and limited osteoinductivity. This study aims to develop composite ...