high performance computing on graphics processing units: hgpu.org

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On the design of architecture-aware algorithms for emerging applications

Seunghwa Kang

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Tags: Algorithms, Benchmarking, Biology, Cell processor, Computational biology, CUDA, Image processing, MapReduce, nVidia, Package, Tesla C1060, Thesis

December 2, 2011 by hgpu

An error correction solver for linear systems: Evaluation of mixed precision implementations

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker

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Tags: Benchmarking, Computer science, CUDA, Mixed precision, nVidia, Tesla S1070

December 2, 2011 by hgpu

GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating

Hao Wu, Junbo Xu, Shengfei Zhang, Hao Wen

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Tags: Algorithms, Benchmarking, Chemistry, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 285, Optimization

November 30, 2011 by hgpu

Applications Performance on GPGPUs with the Fermi Architecture

Ahmed Mohamed Hassan Abdalla

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8400 GS, Performance, Tesla C2050, Tesla M1060, Thesis

November 29, 2011 by hgpu

Numerical Precision and Benchmarking Very-High-Order Integration of Particle Dynamics on GPU Accelerators

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

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Tags: Algorithms, Benchmarking, Computer science, CUDA, N-body simulation, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Tesla C2070

November 27, 2011 by hgpu

A New Compilation Path: From Python/NumPy to OpenCL

Xunhao Li, Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, Python

November 26, 2011 by hgpu

TEG: GPU Performance Estimation Using a Timing Model

Junjie Lai, Andre Seznec

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

November 23, 2011 by hgpu

GPU-Based Image Processing Use Cases: A High-Level Approach

Volkmar Wieser, Clemens Grelck, Holger Schoner, Peter Haslinger, Karoly Bosa, Bernhard Moser

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Tags: Benchmarking, CUDA, Image processing, nVidia, nVidia GeForce GT 425 M, Software Engineering, Virtualization

November 21, 2011 by hgpu

Challenge benchmarks that must be conquered to sustain the gpu revolution

Emily Blem, Matthew Sinclair, Karthikeyan Sankaralingam

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Tags: Benchmarking, Computer science, CUDA, nVidia, Tesla C1060

November 21, 2011 by hgpu

Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 by hgpu

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Matthieu Ospici, Dimitri Komatitsch, Jean-Francois Mehaut, Thierry Deutsch

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Tags: Benchmarking, Computer science, CUDA, Hybrid computing, nVidia, Tesla T10

November 19, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 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

On the design of architecture-aware algorithms for emerging applications

An error correction solver for linear systems: Evaluation of mixed precision implementations

GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating

Applications Performance on GPGPUs with the Fermi Architecture

Numerical Precision and Benchmarking Very-High-Order Integration of Particle Dynamics on GPU Accelerators

A New Compilation Path: From Python/NumPy to OpenCL

TEG: GPU Performance Estimation Using a Timing Model

GPU-Based Image Processing Use Cases: A High-Level Approach

Challenge benchmarks that must be conquered to sustain the gpu revolution

Autotuning GEMMs for Fermi

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

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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