high performance computing on graphics processing units: hgpu.org

hgpu.org » Code generation

Building-Blocks for Performance Oriented DSLs

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

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Programming Languages, Tesla C2050

November 23, 2011 by hgpu

Online Adaptive Code Generation and Tuning

Ananta Tiwari, Jeffrey K. Hollingsworth

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Tags: Code generation, Computer science, Optimization

November 21, 2011 by hgpu

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, Java, nVidia, Optimization, Package, Tesla C2050

November 21, 2011 by hgpu

Neon: A Domain-Specific Programming Language for Image Processing

Brian Guenter, Diego Nehab

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Tags: ATI, ATI Radeon HD 4850, ATI Radeon HD 5870, Code generation, DirectX, High-level Languages, HLSL, Image processing, nVidia, nVidia GeForce GTX 470, Pixel shaders

November 18, 2011 by hgpu

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

Serge Guelton, Francois Irigoin, Ronan Keryell

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Tags: Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Quadro FX 2700 M, Package, Signal processing

November 17, 2011 by hgpu

Automatic transformation and optimization of applications on GPUs and GPU clusters

Wenjing Ma

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Tags: Algorithm optimization, Algorithms, Benchmarking, Code generation, Computer science, CUDA, Data mining, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GX2, Optimization, Tesla T10, Thesis

November 9, 2011 by hgpu

PATUS: A Code Generation and Auto-Tuning Framework For Parallel Stencil Computations

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, CUDA, nVidia, Optimization, Package, Tesla C2050

November 8, 2011 by hgpu

Transformation of Scientific Algorithms to Parallel Computing Code: Single GPU and MPI multi GPU Backends with Subdomain Support

Bjorn Meyer, Christian Plessl, Jens Forstner

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Tags: Algorithms, Code generation, CUDA, FPGA, GPU cluster, MPI, nVidia, nVidia GeForce GTX 480, Tesla C2050

November 5, 2011 by hgpu

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Hanwoong Jung, Youngmin Yi, Soonhoi Ha

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Tags: Code generation, Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Tesla M2050

October 30, 2011 by hgpu

Jit4OpenCL: a compiler from Python to OpenCL

Xunhao Li

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Tags: ATI, ATI Radeon HD 5850, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, OpenCL, Python, Thesis

October 29, 2011 by hgpu

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

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

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Optimization, Tesla C2050

October 28, 2011 by hgpu

Extending Scala with General Purpose GPU Programming

Reidar Beck, Helge Willum Larsen, Tommy Jensen

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Tags: Benchmarking, Code generation, Compilers, Computer science, Java, nVidia, OpenCL, Package, Tesla C870

October 25, 2011 by hgpu

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

Building-Blocks for Performance Oriented DSLs

Online Adaptive Code Generation and Tuning

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Neon: A Domain-Specific Programming Language for Image Processing

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

Automatic transformation and optimization of applications on GPUs and GPU clusters

PATUS: A Code Generation and Auto-Tuning Framework For Parallel Stencil Computations

Transformation of Scientific Algorithms to Parallel Computing Code: Single GPU and MPI multi GPU Backends with Subdomain Support

Automatic CUDA Code Synthesis Framework for Multicore CPU and GPU architectures

Jit4OpenCL: a compiler from Python to OpenCL

Implementing Domain-Specific Languages for Heterogeneous Parallel Computing

Extending Scala with General Purpose GPU Programming

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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