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GPU Scripting and Code Generation with PyCUDA

Andreas Klockner, Nicolas Pinto, Bryan Catanzaro, Yunsup Lee, Paul Ivanov, Ahmed Fasih

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Tags: Code generation, Computer science, CUDA, nVidia, OpenCL, Package, PyOpenCL, Python

April 23, 2013 by hgpu

CMCpy: Genetic Code-Message Coevolution Models in Python

Peter J. Becich, Brian P. Stark, Harish S. Bhat, David H. Ardell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 460, Package, Python

March 31, 2013 by hgpu

OpenOF: Framework for Sparse Non-linear Least Squares Optimization on a GPU

Cornelius Wefelscheid, Olaf Hellwich

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 570, Optimization, Package, Python

March 2, 2013 by hgpu

MATLAB and Python for GPU Computing

Jose Unpingco, Juan Carlos Chaves

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTS, nVidia GeForce 9800 GX2, Python, Signal processing, Tesla C1060, Tesla C2050

February 15, 2013 by hgpu

A multi-lane traffic simulation model via continuous cellular automata

Emanuele Rodaro, Oznur Yeldan

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Tags: Cellular automata, Computer science, CUDA, nVidia, nVidia GeForce GT 555 M, Package, Python

February 9, 2013 by hgpu

Enabling Inter-Machine Parallelism in High-Level Languages with SEJITS and MapReduce

Michael Driscoll, Evangelos Georganas, Penporn Koanantakool

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Tags: Computer science, CUDA, MapReduce, nVidia, Programming techniques, Python, Tesla M2050

February 9, 2013 by hgpu

Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods

Christopher Cooper, Lorena A. Barba

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Tags: Bioinformatics, Biophysics, Boltzmann equation, Computational Physics, Computer science, CUDA, Electrostatics, nVidia, Package, Physics, Poisson Boltzmann, Python, Tesla C2075

January 31, 2013 by hgpu

Particle method on GPU

Jean-Matthieu Etancelin, Georges-Henri Cottet, Christophe Picard, Franck Perignon

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Tags: Algorithms, ATI, ATI Radeon HD 6770 M, Computer science, OpenCL, Python

January 31, 2013 by hgpu

GPGPU-BASED Cortical Modeling

Theodore Hilk

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Tags: Algorithms, ATI, ATI Radeon HD 6870, ATI Radeon HD 7970, Image processing, OpenCL, PyOpenCL, Python

January 11, 2013 by hgpu

Multi-level Debugging for Multi-stage, Parallelizing Compilers

Richard Xia, Tayfun Elmas, Shoaib Ashraf Kamil, Armando Fox, Koushik Sen

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

December 12, 2012 by hgpu

A compiler toolkit for array-based languages targeting CPU/GPU hybrid systems

Rahul Garg, Laurie Hendren

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Tags: ATI, ATI Radeon HD 5850, ATI Radeon HD 7970, Code generation, Compilers, Computer science, OpenCL, Package, Python, Tesla C2050

November 26, 2012 by hgpu

PyFAI, a versatile library for azimuthal regrouping

Jerome Kieffer, Dimitrios Karkoulis

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Tags: ATI, ATI FirePro V7800, Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Python, Tesla C2075

November 25, 2012 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

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high performance computing on graphics processing units: hgpu.org

GPU Scripting and Code Generation with PyCUDA

CMCpy: Genetic Code-Message Coevolution Models in Python

OpenOF: Framework for Sparse Non-linear Least Squares Optimization on a GPU

MATLAB and Python for GPU Computing

A multi-lane traffic simulation model via continuous cellular automata

Enabling Inter-Machine Parallelism in High-Level Languages with SEJITS and MapReduce

Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods

Particle method on GPU

GPGPU-BASED Cortical Modeling

Multi-level Debugging for Multi-stage, Parallelizing Compilers

A compiler toolkit for array-based languages targeting CPU/GPU hybrid systems

PyFAI, a versatile library for azimuthal regrouping

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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