high performance computing on graphics processing units: hgpu.org

Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Milind Chabbi, Karthik Murthy, Pavan Balaji, Keith R. Bisset, James Dinan, Wu-chun Feng, John Mellor-Crummey, Xiaosong May, Rajeev Thakur

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Optimization, Seismology, Tesla M2050

April 22, 2013 by hgpu

Multi-GPU Island-Based Genetic Algorithm

Namrata Mahakalkar, A. R. Mahajan

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Tags: Algorithms, Computer science, CUDA, Genetic programming, GPU cluster, nVidia, nVidia GeForce GTX 580

March 20, 2013 by hgpu

Parallel Shooting and Bouncing Ray Method on GPU Clusters for Analysis of Electro-Magnetic Scattering

Pengcheng Gao, Yubo Tao, Hai Lin

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Tags: CUDA, Electrodynamics, GPU cluster, MPI, nVidia, nVidia GeForce GTX 580, Tesla C2050

February 27, 2013 by hgpu

A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Shuaiwen Song, Chunyi Su, Barry Rountree, Kirk W. Cameron

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Tags: Biomolecules, Computer science, CUDA, Electrostatics, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, Tesla C2075, Tesla M2090

February 18, 2013 by hgpu

Evaluation of Standardized Password-based Key Derivation against Parallel Processing Platforms

Markus Durmuth, Tim Guneysu, Markus Kasper, Christof Paar, Tolga Yalcin, Ralf Zimmermann

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Tags: Computer science, CUDA, FPGA, GPU cluster, nVidia, Review, Security, Tesla C2070

February 14, 2013 by hgpu

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Miguel Lopez-Portugues, Yuri Alvarez, Jesus A. Lopez-Fernandez, Cebrian Garcia, Rafael G. Ayestaran, Fernando Las-Heras

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Tags: CUDA, Electrodynamics, GPU cluster, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 680, Signal processing

February 6, 2013 by hgpu

Efficient Implementation of MrBayes on multi-GPU

Jie Bao, Hongju Xia, Jianfu Zhou, Xiaoguang Liu, Gang Wang

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Tags: Bayesian, Biology, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, Package, Task scheduling

January 28, 2013 by hgpu

A Multi-GPU Programming Library for Real-Time Applications

Sebastian Schaetz, Martin Uecker

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Tags: Algorithms, CUDA, GPU cluster, Image reconstruction, Magnetic resonance imaging, MPI, MRI, nVidia, nVidia GeForce GTX 580, Performance, Signal processing

January 8, 2013 by hgpu

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

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

DistCL: A Framework for the Distributed Execution of OpenCL Kernels

GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting

Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies

A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets

On the Efficacy of GPU-Integrated MPI for Scientific Applications

Multi-GPU Island-Based Genetic Algorithm

Parallel Shooting and Bouncing Ray Method on GPU Clusters for Analysis of Electro-Magnetic Scattering

A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

Evaluation of Standardized Password-based Key Derivation against Parallel Processing Platforms

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Efficient Implementation of MrBayes on multi-GPU

A Multi-GPU Programming Library for Real-Time Applications

Recent source codes

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

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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