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Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 2015 by hgpu

Extending the Gotran framework: LATEX and GPU acceleration

George Bahijevitch Bahij

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Tags: Computational modelling, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan, ODEs, Ordinary differential equations, Python, Thesis

February 6, 2015 by hgpu

Montblanc: GPU accelerated Radio Interferometer Measurement Equations in support of Bayesian Inference for Radio Observations

Simon Perkins, Patrick Maraism, Jonathan Zwart, Iniyan Natarajan, Oleg Smirnov

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Tags: Astrophysics, Bayesian, Computer science, CUDA, Instrumentation and Methods for Astrophysics, nVidia, Package, Python, Tesla K40

February 2, 2015 by hgpu

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Leonard Truong, Chick Markley, Armando Fox

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Tags: Code generation, Computer science, OpenCL, Python

January 8, 2015 by hgpu

PyFAI: a Python library for high performance azimuthal integration on GPU

Jerome Kieffer, Giannis Ashiotis

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Tags: Astrophysics, ATI, ATI FirePro V7800, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, OpenCL, Package, PyOpenCL, Python, Tesla K20

December 22, 2014 by hgpu

Bayesian neural networks for detecting epistasis in genetic association studies

Andrew L Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, CULA, Neural networks, nVidia, Package, Python

December 15, 2014 by hgpu

Theano-based Large-Scale Visual Recognition with Multiple GPUs

Weiguang Ding, Ruoyan Wang, Fei Mao, Graham Taylor

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Tags: Computer science, CUDA, Data parallelism, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Python

December 9, 2014 by hgpu

pyMIC: A Python Offload Module for the Intel Xeon Phi Coprocessor

Michael Klemm, Jussi Enkovaara

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Tags: Computer science, Intel Xeon Phi, Python

December 1, 2014 by hgpu

A Framework for Composing High-Performance OpenCL from Python Descriptions

Michael Anderson

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 480, OpenCL, Optical flow, Python, Thesis

November 29, 2014 by hgpu

A GPU-based framework for efficient image processing

Per Karlsson

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Tags: CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 760, OpenCL, OpenGL, Python, Thesis

November 20, 2014 by hgpu

CUDArray: CUDA-based NumPy

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Neural networks, nVidia, Package, Python

November 16, 2014 by hgpu

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

Di Wu, Ling Shao

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Tags: CNN, Computer science, CUDA, Neural networks, nVidia, Package, Python, Rendering

October 4, 2014 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

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Extending the Gotran framework: LATEX and GPU acceleration

Montblanc: GPU accelerated Radio Interferometer Measurement Equations in support of Bayesian Inference for Radio Observations

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

PyFAI: a Python library for high performance azimuthal integration on GPU

Bayesian neural networks for detecting epistasis in genetic association studies

Theano-based Large-Scale Visual Recognition with Multiple GPUs

pyMIC: A Python Offload Module for the Intel Xeon Phi Coprocessor

A Framework for Composing High-Performance OpenCL from Python Descriptions

A GPU-based framework for efficient image processing

CUDArray: CUDA-based NumPy

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

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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