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Theano-MPI: a Theano-based Distributed Training Framework

He Ma, Fei Mao, Graham W. Taylor

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Tags: Computer science, CUDA, Deep learning, Machine learning, MPI, nVidia, Package, Tesla K20, Tesla K80

May 28, 2016 by hgpu

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Ebad Salehi

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Tags: Compression, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20, Thesis

May 21, 2016 by hgpu

Exposing Errors Related to Weak Memory in GPU Applications

Tyler Sorensen, Alastair F. Donaldson

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Tags: Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia Quadro K5200, Tesla C2050, Tesla C2075, Tesla K20

May 3, 2016 by hgpu

Adaptive GPU Array Layout Auto-Tuning

Nicolas Weber, Michael Goesele

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Tags: Computer science, CUDA, Linear Algbera, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K20

April 29, 2016 by hgpu

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla K20

April 16, 2016 by hgpu

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

Christos Margiolas, Michael F.P. O'Boyle

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Tags: AMD Radeon R9 295X2, ATI, Computer science, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

April 12, 2016 by hgpu

GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

X. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

X. J. A. Bellekens, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: CUDA, nVidia, Security, Tesla K20

April 9, 2016 by xhgpu

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

Gabriel O. Trisca

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Tags: Aparapi, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, OpenCL, RNN, Seismology, Sensing, Signal denoising, Tesla K20, Thesis

March 20, 2016 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

Theano-MPI: a Theano-based Distributed Training Framework

Architecture-Adaptive Code Variant Tuning

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Exposing Errors Related to Weak Memory in GPU Applications

Adaptive GPU Array Layout Auto-Tuning

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Portable and Transparent Software Managed Scheduling on Accelerators for Fair Resource Sharing

GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

Dynamic Sparse-Matrix Allocation on GPUs

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

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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