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Throughput-Oriented Analytical Models for Performance Estimation on Programmable Hardware Accelerators

Junjie Lai

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Tags: Analytical model, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Thesis

August 28, 2013 by hgpu

Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

Joao V. F. Lima, Nicolas Maillard, Franciois Broquedis, Thierry Gautier, Bruno Raffin

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Tags: Benchmarking, Computer science, Intel, Intel Phi, Performance

August 26, 2013 by hgpu

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

Muhammed Al-Mulhem, Abdulah AlDhamin, Raed Al-Shaikh

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Tags: Algorithms, Benchmarking, Bioinformatics, Computer science, CUDA, Linear Algebra, Matrix multiplication, MPI, nVidia, nVidia GeForce GTX 460

July 19, 2013 by hgpu

Benchmarking Intel Xeon Phi to Guide Kernel Design

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel, Intel Phi, Performance

July 14, 2013 by hgpu

Comparative study of parallel programming models for multicore computing

Akhtar Ali

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Tags: Benchmarking, Computer science, DSP, Heterogeneous systems, nVidia, OpenCL, OpenMP, Tesla M2050, Thesis

July 7, 2013 by hgpu

Towards Rapid Prototyping of Parallel and HPC Applications (GPU Focus)

Mohammed S. Al-Mahfoudh

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 440, Thesis

June 28, 2013 by hgpu

GPUburn: A System to Test and Mitigate GPU Hardware Failures

David Defour, Eric Petit

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Tags: Benchmarking, Computer science, CUDA, Fault tolerance, nVidia, OpenCL, Tesla D870

June 2, 2013 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Tesla C2050, Tesla C2070

May 30, 2013 by hgpu

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

A.-E Hugo A. Guermouche P.-A. Wacrenier R. Namyst

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Tesla M2070

May 23, 2013 by hgpu

Evaluating the Performance of Legacy Applications on Emerging Parallel Architectures

Simon John Pennycook

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Tags: Algorithms, ATI, ATI FirePro V7800, Benchmarking, Computer science, CUDA, MPI, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9800 GT, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C1060, Tesla C2050, Thesis

May 21, 2013 by hgpu

Implementing Continuous Integration Software in an Established Computational Chemistry Software Package

Robin M. Betz, Ross C. Walker

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Tags: Benchmarking, Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, Software Engineering, Tesla M2090

May 21, 2013 by hgpu

An Investigation of the Performance Portability of OpenCL

S.J. Pennycook, S.D. Hammond, S.A. Wright, J.A. Herdman, I. Miller, S.A. Jarvis

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Tags: ATI, ATI FirePro V7800, Benchmarking, Computer science, CUDA, Fortran, MPI, nVidia, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

May 21, 2013 by hgpu

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

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high performance computing on graphics processing units: hgpu.org

Throughput-Oriented Analytical Models for Performance Estimation on Programmable Hardware Accelerators

Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

On Benchmarking the Matrix Multiplication Algorithm using OpenMP, MPI and CUDA Programming Languages

Benchmarking Intel Xeon Phi to Guide Kernel Design

Comparative study of parallel programming models for multicore computing

Towards Rapid Prototyping of Parallel and HPC Applications (GPU Focus)

GPUburn: A System to Test and Mitigate GPU Hardware Failures

Performance Tradeoff Spectrum of Integer and Floating Point Applications

Composing multiple StarPU applications over heterogeneous machines: a supervised approach

Evaluating the Performance of Legacy Applications on Emerging Parallel Architectures

Implementing Continuous Integration Software in an Established Computational Chemistry Software Package

An Investigation of the Performance Portability of OpenCL

Recent source codes

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

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