AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

With Saratoga and Del Mar about to kick off, now’s the perfect time to sharpen your skills for one of horse racing’s most exciting bets: the Pick 5. It’s a high-stakes wager that can deliver massive ...

The execution model in Nvidia GPUs is SIMT (Single‑Instruction, Multiple‑Threads). At the hardware level, the GPU schedules and executes threads in groups of 32 called "warps". In this "load-store" ...

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 ...

IRMSD is a Python library for computing the optimal root-mean-square-deviation between pairs of structures (e.g., protein conformations). It is based on the Theobald QCP method, and because of an ...

PDC Center for High Performance Computing, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, ...

Abstract: This letter presents a parallel frequency-domain finite-difference (FDFD) algorithm based on multi-graphic processing unit (GPU) applied to electromagnetic scattering computations to enhance ...

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...

Abstract: Sparse matrix-matrix multiplication is a critical kernel for several scientific computing applications, especially the setup phase of algebraic multigrid. The MPI+X programming model, which ...