high performance computing on graphics processing units: hgpu.org

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith

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Tags: ATI, ATI Radeon HD 7970, Biology, Neuroscience, nVidia, nVidia GeForce GTX 280, OpenCL, Package, PyOpenCL, Python

January 9, 2014 by hgpu

PySPH: A Python framework for SPH

Kunal Puri, Prabhu Ramachandran, Pankaj Pandey, Chandrashekhar Kaushik, Pushkar Godbole

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Tags: Fluid dynamics, OpenCL, Package, Python

January 6, 2014 by hgpu

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

Ethel Bardsley, Alastair F. Donaldson

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

December 20, 2013 by hgpu

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

S. Lewis, D. Ireland, W. Vanderbauwhede

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Tags: ATI, Bayesian, OpenCL, Package, Physics

December 12, 2013 by hgpu

Adaptive implementation selection in the SkePU skeleton programming library

Usman Dastgeer, Lu Li, Christoph Kessler

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Tags: Computer science, CUDA, Machine learning, nVidia, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

November 19, 2013 by hgpu

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Michal Januszewski, Marcin Kostur

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Tags: Code generation, Computational Physics, CUDA, Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 285, OpenCL, Package, Physics, Python, Tesla C1060, Tesla C2050, Tesla K10, Tesla K20

November 12, 2013 by hgpu

Computing finite models using free Boolean generators

Zarko Mijajlovic, Aleksandar Pejovic

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Tags: Computer science, Logic in Computer Science, OpenCL, Package, PyOpenCL

October 29, 2013 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

Fast Acceleration of 2D Wave Propagation Simulations Using Modern Computational Accelerators

Autotuning Programs with Algorithmic Choice

clpeak – peak performance of your opencl device

Adaptation of an acoustic propagation model to the parallel architecture of a graphics processor

Towards Portable Performance for Explicit Hydrodynamics Codes

Nengo: a Python tool for building large-scale functional brain models

PySPH: A Python framework for SPH

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

Adaptive implementation selection in the SkePU skeleton programming library

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Computing finite models using free Boolean generators

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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