techtimes

AMD and Intel’s ACE Locks In x86 AI Compute Standard, Replacing Intel’s Older AMX

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 ...
C&EN

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

§Contributed equally to this work. Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These ...
GitHub

A curated list of resources for learning and exploring Triton, OpenAI's programming language for writing efficient GPU code.

This project is a step-by-step learning journey where we implement various types of Triton kernels—from the simplest examples to more advanced applications—while exploring GPU programming with Triton.
GitHub

Cyclops Tensor Framework (CTF)

Cyclops is a parallel (distributed-memory) numerical library for multidimensional arrays (tensors) in C++ and Python. Quick documentation links: C++ and Python. Broadly, Cyclops provides tensor ...
C&EN

VeloxChem: GPU-Accelerated Fock Matrix Construction Enabling Complex Polarization Propagator Simulations of Circular Dichroism Spectra of G-Quadruplexes

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, ...
IEEE

A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs

Abstract: This paper presents a performance modeling and optimization analysis tool to predict and optimize the performance of sparse matrix-vector multiplication (SpMV) on GPUs. We make the following ...
Scientific Research Publishing

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique ()

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.