high performance computing on graphics processing units: hgpu.org

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Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Faiz Khan, Vincent Foley-Bourgon, Sujay Kathrotia, Erick Lavoie, Laurie Hendren

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Java, nVidia, OpenCL, Package, Tesla C2050

June 23, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

High Performance Non-Blocking Collective Communication for Next Generation Infiniband Clusters

Krishna Kandalla

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Tags: Benchmarking, Computer science, InfiniBand, Intel Xeon Phi, Linear Algebra, MPI

March 14, 2014 by hgpu

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

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

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

A Similarity-Based Analysis Tool for Scientific Application Porting

Wei Ding

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenMP, Performance, Portability, Tesla C2070, Thesis

February 17, 2014 by hgpu

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Sadaf Alam, Ugo Varetto

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K20, Tesla M2090

February 15, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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

February 11, 2014 by hgpu

Benchmarks for Intel MIC Architecture

Emprise Y.K. Chan

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Monte Carlo simulation

February 9, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

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

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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, Tesla K20

February 8, 2014 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

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Parallel Support Vector Machines in Practice

High Performance Non-Blocking Collective Communication for Next Generation Infiniband Clusters

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

A Similarity-Based Analysis Tool for Scientific Application Porting

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Benchmarking the Intel Xeon Phi Coprocessor

Benchmarks for Intel MIC Architecture

Developmental Directions in Parallel Accelerators

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

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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