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CuPP – A framework for easy CUDA integration

Jens Breitbart

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package

November 4, 2010 by hgpu

Brook for GPUs: Stream Computing on Graphics Hardware

I. Buck, T. Foley, D. Horn, J. Sugerman, K. Mike, H. Pat

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Tags: ATI, ATI Radeon 9800 XT, ATI Stream, Brook, Computer science, High-level Languages, nVidia, nVidia GeForce FX 5900 Ultra, OpenGL, Stream Computing

November 3, 2010 by hgpu

Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi, Sam S. Stone, David B. Kirk, Wen-mei W. Hwu

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Performance, Programming techniques

October 27, 2010 by hgpu

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

Andreas Klockner,Nicolas Pinto,Yunsup Lee,Bryan Catanzaro,Paul Ivanov,Ahmed Fasih

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package

October 27, 2010 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

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