high performance computing on graphics processing units: hgpu.org

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Mapping parallel programs to heterogeneous multi-core systems

Dominik Grewe

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Thesis

June 5, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 2014 by hgpu

Engineering a static verification tool for GPU kernels

Ethel Bardsley, Adam Betts, Nathan Chong, Peter Collingbourne, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema, Daniel Liew, Shaz Qadee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Python

May 25, 2014 by hgpu

Optimized Composition: Generating Efficient Code for Heterogeneous Systems from Multi-Variant Components, Skeletons and Containers

Christoph Kessler, Usman Dastgeer, Lu Li

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Package, Review, Tesla C2050

May 15, 2014 by hgpu

Geometric Algebra Enhanced Precompiler for C++, OpenCL and Mathematica’s OpenCLLink

Patrick Charrier, Mariusz Klimek, Christian Steinmetz, Dietmar Hildenbrand

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Package

May 12, 2014 by hgpu

Improving the Programmability of GPU Architectures

Cedric Nugteren

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Thesis

May 7, 2014 by hgpu

Sparser, Better, Faster GPU Parsing

David Hall, Taylor Berg-Kirkpatrick, John Canny, Dan Klein

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Tags: Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance

April 21, 2014 by hgpu

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

S. Rit, M. Vila Oliva, S. Brousmiche, R. Labarbe, D. Sarrut, G. C. Sharp

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Tags: Computed tomography, CT, CUDA, Medicine, nVidia, OpenCL, Package, Tesla M2090

April 18, 2014 by hgpu

Initial condition for efficient mapping of level set algorithms on many-core architectures

Gabor Janos Tornai, Gyorgy Cserey

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Tags: Differential equations, nVidia, nVidia GeForce GTX 780, OpenCL, Package, Partial differential equations, PDEs, Signal processing

March 14, 2014 by hgpu

Massively parallel read mapping on GPUs with PEANUT

Johannes Koster, Sven Rahmann

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Tags: Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780, OpenCL, Package, Python

March 10, 2014 by hgpu

QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

Adam Gudys, Sebastian Deorowicz

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Tags: ATI, ATI Radeon HD 7970, Bioinformatics, Biology, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package, Sequence alignment

March 4, 2014 by hgpu

Optimizing Performance of Stencil Code with SPL Conqueror

Alexander Grebhahn, Norbert Siegmund, Sven Apel, Sebastian Kuckuk, Christian Schmitt, Harald Kostler

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Tags: Computer science, CUDA, Differential equations, Finite difference, Image processing, nVidia, OpenCL, Package, Partial differential equations, PDEs, Tesla K20

February 17, 2014 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Mapping parallel programs to heterogeneous multi-core systems

Loo.py: transformation-based code generation for GPUs and CPUs

Engineering a static verification tool for GPU kernels

Optimized Composition: Generating Efficient Code for Heterogeneous Systems from Multi-Variant Components, Skeletons and Containers

Geometric Algebra Enhanced Precompiler for C++, OpenCL and Mathematica’s OpenCLLink

Improving the Programmability of GPU Architectures

Sparser, Better, Faster GPU Parsing

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

Initial condition for efficient mapping of level set algorithms on many-core architectures

Massively parallel read mapping on GPUs with PEANUT

QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

Optimizing Performance of Stencil Code with SPL Conqueror

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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