high performance computing on graphics processing units: hgpu.org

Micah J. Best, Alexandra Fedorova, Ryan Dickie, Andrea Tagliasacchi, Alex Couture-Beil, Craig Mustard, Shane Mottishaw, Aron Brown, Zhi Feng Huang, Xiaoyuan Xu, Nasser Ghazali, Andrew Brownsword

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Tags: Algorithms, Computer science, Games, Performance

September 16, 2011 by hgpu

A Light-Weight Approach to Dynamical Runtime Linking Supporting Heterogenous, Parallel, and Reconfigurable Architectures

Rainer Buchty, David Kramer, Mario Kicherer, Wolfgang Karl

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Tags: Algorithms, Computer science, Heterogeneous systems, Programming techniques

September 16, 2011 by hgpu

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Florian Worsdorfer, Florian Stock, Eduardo Bayro-Corrochano, Dietmar Hildenbrand

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Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization, Package, Performance

September 16, 2011 by hgpu

Parallel Medical Image Reconstruction: From Graphics Processors to Grids

Maraike Schellmann, Sergei Gorlatch, Dominik Meilander, Thomas Kosters, Klaus Schafers, Frank Wubbeling, Martin Burger

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Tags: Algorithms, Cell processor, Grid, Image reconstruction, Medicine, PET, PET scanning, Tomography

September 16, 2011 by hgpu

A Generic Approach to Topic Models

Gregor Heinrich

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Tags: Algorithms, Bayesian, Computer science

September 16, 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

Randomized selection on the GPU

Hybrid smoothed particle hydrodynamics

Simpler and faster HLBVH with work queues

A GPU-tailored approach for training kernelized SVMs

Stream computing on graphics hardware

Using many-core hardware to correlate radio astronomy signals

NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce

Searching for Concurrent Design Patterns in Video Games

A Light-Weight Approach to Dynamical Runtime Linking Supporting Heterogenous, Parallel, and Reconfigurable Architectures

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Parallel Medical Image Reconstruction: From Graphics Processors to Grids

A Generic Approach to Topic Models

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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