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Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Martin Sandrieser, Siegfried Benkner, Sabri Pllana

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, OpenMP, Presentation

August 22, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

A programming model for GPU-based parallel computing with scalability and abstraction

Balazs Domonkos, Gabor Jakab

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Tags: 3D Graphics and Realism, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, OpenMP, Programming techniques

August 22, 2011 by hgpu

Sponge: portable stream programming on graphics engines

Amir H. Hormati, Mehrzad Samadi, Mark Woh, Trevor Mudge, Scott Mahlke

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization, Performance, Programming Languages

August 21, 2011 by hgpu

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

Jing Guo, Jeyarajan Thiyagalingam, Sven-Bodo Scholz

Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages

August 21, 2011 by hgpu

GRace: a low-overhead mechanism for detecting data races in GPU programs

Mai Zheng, Vignesh T. Ravi, Feng Qin, Gagan Agrawal

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming techniques, Tesla C1060

August 20, 2011 by hgpu

A framework for dynamically instrumenting GPU compute applications within GPU Ocelot

Naila Farooqui, Andrew Kerr, Gregory Diamos, S. Yalamanchili, K. Schwan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Programming Languages, PTX

August 19, 2011 by hgpu

Extending abstract GPU APIs to shared memory

Ferosh Jacob

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, OpenMP, Presentation, Programming Languages, Programming techniques

August 19, 2011 by hgpu

A cluster for CS education in the manycore era

Joel C. Adams, Kathy Hoobeboom, Jonathan Walz

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, nVidia, OpenCL, OpenMP, OpenMPI, Performance

August 19, 2011 by hgpu

Caracal: dynamic translation of runtime environments for GPUs

Rodrigo Dominguez, Dana Schaa, David Kaeli

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Programming Languages, PTX

August 19, 2011 by hgpu

Auto-tuning SkePU: a multi-backend skeleton programming framework for multi-GPU systems

Usman Dastgeer, Johan Enmyren, Christoph W. Kessler

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Tags: Computer science, CUDA, Data parallelism, nVidia, OpenCL, OpenMP, Package, Performance, Programming techniques, Tesla C1060

August 19, 2011 by hgpu

SkePU: a multi-backend skeleton programming library for multi-GPU systems

Johan Enmyren, Christoph W. Kessler

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Tags: Computer science, CUDA, Data parallelism, nVidia, OpenCL, OpenMP, Package, Performance, Programming techniques, Tesla C1060

August 18, 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

Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Accelerating Haskell array codes with multicore GPUs

A programming model for GPU-based parallel computing with scalability and abstraction

Sponge: portable stream programming on graphics engines

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

GRace: a low-overhead mechanism for detecting data races in GPU programs

A framework for dynamically instrumenting GPU compute applications within GPU Ocelot

Extending abstract GPU APIs to shared memory

A cluster for CS education in the manycore era

Caracal: dynamic translation of runtime environments for GPUs

Auto-tuning SkePU: a multi-backend skeleton programming framework for multi-GPU systems

SkePU: a multi-backend skeleton programming library for multi-GPU 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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