high performance computing on graphics processing units: hgpu.org

hgpu.org » Programming techniques

A Heterogeneous Parallel Framework for Domain-Specific Languages

Kevin J. Brown, Arvind K. Sujeeth, HyoukJoong Lee, Tiark Rompf, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Programming techniques, Tesla C2050

October 14, 2011 by hgpu

State of The Art Report on GPU

Rita Borgo, Ken Brodlie

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Tags: Computer science, CUDA, DirectX, GLSL, GPU cluster, Hardware, HLSL, Larrabee, nVidia, OpenGL, Programming techniques, Review

October 13, 2011 by hgpu

Performance Characterization and Optimization of Atomic Operations on AMD GPUs

Marwa Elteir, Heshan Lin, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, OpenCL, Optimization, Performance, Programming techniques

October 11, 2011 by hgpu

Towards an Effective Unified Programming Model for Many-Cores

Ana Lucia Varbanescu, Pieter Hijma, Rob V. van Nieuwpoort, Henri E. Bal

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Tags: ATI, Cell processor, Computer science, nVidia, OpenCL, Optimization, Programming techniques

October 10, 2011 by hgpu

Programming framework for clusters with heterogeneous accelerators

Anson H.T. Tse, David B. Thomas, K.H. Tsoi, Wayne Luk

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Tags: Computer science, Finance, FPGA, Heterogeneous systems, Physics, Programming techniques, Tesla

October 8, 2011 by hgpu

A Framework for Automatic OpenMP Code Generation

A. Raghesh

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Tags: Code generation, Computer science, OpenCL, Optimization, Programming techniques, Thesis

October 7, 2011 by hgpu

Static GPU threads and an improved scan algorithm

Jens Breitbart

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 280, OpenCL, Performance, Presentation, Programming techniques

October 6, 2011 by hgpu

A Massive Data Parallel Computational Framework on Petascale/Exascale Hybrid Computer Systems

Marek Blazewicz, Steven R. Brandt, Peter Diener, David M. Koppelman, Krzysztof Kurowski, Frank Loffler, Erik Schnetter, Jian Tao

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, nVidia, OpenCL, Optimization, Package, Programming techniques, Tesla S1070

October 4, 2011 by hgpu

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Fawnizu Azmadi Hussin, Anas Mohd Nazlee, Noohul Basheer Zain Ali

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Tags: Algorithms, Computer science, CUDA, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques, Security

October 3, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

Christopher J. Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, Emmett Witchel

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Tags: Computer science, CUDA, HLSL, nVidia, nVidia GeForce GT 230, nVidia GeForce GTX 470, nVidia GeForce GTX 580, OpenCL, Operating systems, Performance, Programming techniques

October 2, 2011 by hgpu

Customizable Domain-Specific Computing

Jason Cong, Glenn Reinman, Alex Bui, Vivek Sarkar

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Tags: Computer science, FPGA, nVidia, Performance, Programming techniques, Tesla C1060

October 1, 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

A Heterogeneous Parallel Framework for Domain-Specific Languages

State of The Art Report on GPU

Performance Characterization and Optimization of Atomic Operations on AMD GPUs

Towards an Effective Unified Programming Model for Many-Cores

Programming framework for clusters with heterogeneous accelerators

A Framework for Automatic OpenMP Code Generation

Static GPU threads and an improved scan algorithm

A Massive Data Parallel Computational Framework on Petascale/Exascale Hybrid Computer Systems

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

A New Class of Parallel Scheduling Algorithms

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

Customizable Domain-Specific Computing

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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