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Lossless Compression of Variable-Precision Floating-Point Buffers on GPUs

Jeff Pool, Anselmo Lastra, Montek Singh

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Tags: Compression, Computer science, Programming techniques

January 6, 2012 by hgpu

A GPU Implementation of Inclusion-based Points-to Analysis

Mario Mendez-Lojo, Martin Burtscher, Keshav Pingali

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Package, Performance, Programming techniques, Tesla C2070

January 5, 2012 by hgpu

PARRAY: A Unifying Array Representation for Heterogeneous Parallelism

Yifeng Chen, Xiang Cui, Hong Mei

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Tags: Code generation, Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, MPI, nVidia, Optimization, Performance, Programming techniques, Pthreads, Tesla C1060

January 5, 2012 by hgpu

Programming of shared memory GPUs shared memory systems

Jean-Philippe Bergeron

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, Optimization, Programming techniques

January 2, 2012 by hgpu

OpenCL in Action: How to Accelerate Graphics and Computations

Matthew Scarpino

Tags: Book, Computer science, OpenCL, Programming techniques, Tutorial

December 26, 2011 by hgpu

Reducing the Size of Nurbs Controls Nets Using Genetic Algorithms and CUDA

Matthew J. O'Neal, Cameron J. Turner

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Programming techniques

December 23, 2011 by hgpu

Parallel programming with inductive synthesis

Shaon Barman, Rastislav Bodik, Sagar Jain, Yewen Pu, Saurabh Srivastava, Nicholas Tung

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques

December 19, 2011 by hgpu

Leveraging Parallelism with CUDA and OpenCL

S. Park, D. Shires, J. Ross, D. Richie

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Tags: AMD FireStream 9250, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Programming techniques, Tesla C1060

December 18, 2011 by hgpu

Code Optimization Techniques for Graphics Processing Units

Fernando Magno Quintao Pereira

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Tags: Algorithms, Book, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 9400 M, Optimization, Package, Programming techniques, PTX, Tutorial

December 17, 2011 by hgpu

Customizable Memory Schemes for Data Parallel Accelerators

Chunyang Gou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 17, 2011 by hgpu

Evaluating graph coloring on GPUs

A.V. Pascal Grosset, Peihong Zhu, Shusen Liu, Suresh Venkatasubramanian, Mary Hall

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Programming techniques

December 11, 2011 by hgpu

Directives Based Programming of GPU Accelerated Systems

Saif Mulla

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Tags: Computer science, CUDA, Molecular dynamics, nVidia, Programming techniques, Tesla C2050, Thesis

November 29, 2011 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

Lossless Compression of Variable-Precision Floating-Point Buffers on GPUs

A GPU Implementation of Inclusion-based Points-to Analysis

PARRAY: A Unifying Array Representation for Heterogeneous Parallelism

Programming of shared memory GPUs shared memory systems

OpenCL in Action: How to Accelerate Graphics and Computations

Reducing the Size of Nurbs Controls Nets Using Genetic Algorithms and CUDA

Parallel programming with inductive synthesis

Leveraging Parallelism with CUDA and OpenCL

Code Optimization Techniques for Graphics Processing Units

Customizable Memory Schemes for Data Parallel Accelerators

Evaluating graph coloring on GPUs

Directives Based Programming of GPU Accelerated Systems

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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