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A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Allison S. Gehrke, Ilkyeun Ra, Daniel A. Connors

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Tags: Code generation, Computer science, Numerical simulation, nVidia, Optimization, Performance, PTX

October 5, 2011 by hgpu

Flexible, high performance convolutional neural networks for image classification

Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Benchmarking, Computer science, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 580

October 5, 2011 by hgpu

Architecture-Aware Optimization on a 1600-core Graphics Processor

Mayank Daga, Thomas R.W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, Cluster computing, Computer science, CUDA, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Tesla C2050

October 4, 2011 by hgpu

Fine-grained Parallel ILU Preconditioners with Fill-ins for Multi-core CPUs and GPUs

Vincent Heuveline, Dimitar Lukarski and Jan-philipp Weiss

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Source codes

Tags: Algorithms, Computer science, FEM, Finite element method, Heterogeneous systems, Numerical simulation, OpenCL, Package, Partial differential equations, PDEs

October 4, 2011 by hgpu

GPU Algorithms for Diamond-based Multiresolution Terrain Processing

M. Adil Yalcin, Kenneth Weiss, Leila De Floriani

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Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, OpenGL, Rendering, Tessellation, Visualization

October 4, 2011 by hgpu

Berkeley Dwarfs on CUDA

Paul Springer

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

October 4, 2011 by hgpu

Comparing Parallel Simulation of Social Agents using Cilk and OpenCL

Dominik Moser, Andreas Riener, Kashif Zia, Alois Ferscha

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Tags: Computer science, OpenCL, Sensing

October 4, 2011 by hgpu

Optimization of the Gaussian Mixture Model Evaluation on GPU

Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 260, OpenCL, Optimization, Speech recognition

October 4, 2011 by hgpu

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

W. Zuo, A. McNeil, M. Wetter, E.S. Lee

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Tags: Algorithms, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Raytracing

October 4, 2011 by hgpu

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Fawnizu Azmadi Hussin, Anas Mohd Nazlee, Noohul Basheer Zain Ali

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Tags: Algorithms, Computer science, CUDA, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques, Security

October 3, 2011 by hgpu

Parallel Game Tree Search Using GPU

L'ubomir Lackovic

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Games, Graph theory, nVidia, nVidia GeForce GTX 460

October 3, 2011 by hgpu

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, OpenCL, Optimization

October 3, 2011 by hgpu

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Flexible, high performance convolutional neural networks for image classification

Architecture-Aware Optimization on a 1600-core Graphics Processor

Fine-grained Parallel ILU Preconditioners with Fill-ins for Multi-core CPUs and GPUs

GPU Algorithms for Diamond-based Multiresolution Terrain Processing

Berkeley Dwarfs on CUDA

Comparing Parallel Simulation of Social Agents using Cilk and OpenCL

Optimization of the Gaussian Mixture Model Evaluation on GPU

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Parallel Game Tree Search Using GPU

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Recent source codes

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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