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Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

Mean shift for graph bundling

Ozan Ersoy, Christophe Hurter, Alexandru Telea

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Tags: Computer science, Graph theory, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, OpenGL

October 13, 2012 by hgpu

An implicit Tensor-Mass solver on the GPU for soft bodies simulation

X. Faure, F. Zara, F. Jaillet, J-M. Moreau

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Tags: 3D Graphics and Realism, Computer science, FEM, Finite element method, nVidia, nVidia GeForce GTX 560, OpenCL, Virtual reality

October 13, 2012 by hgpu

AeminiumGPU: An Intelligent Framework for GPU Programming

Alcides Fonseca, Bruno Cabral

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Source codes

Tags: Computer science, Heterogeneous systems, Java, MapReduce, nVidia, nVidia GeForce GTX 285, OpenCL, Package

October 13, 2012 by hgpu

.NET High Performance Computing

Hsuan-Hsiu Ou

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GTX 480, Task scheduling, Thesis

October 13, 2012 by hgpu

Parallel H-Tree Based Data Cubing on Graphics Processors

Baoyuan Wang, Yizhou Yu

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce 8800 GTX

October 13, 2012 by hgpu

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Marek Palkowski, Wlodzimierz Bielecki

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Tags: Computer science, CUDA, nVidia, Performance, Tesla S1070

October 13, 2012 by hgpu

Extendable Pattern-Oriented Optimization Directives (extended version)

Huimin Cui, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng, Dongrui Fan

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 285, Optimization, Performance

October 13, 2012 by hgpu

Mesh Independent Loop Fusion for Unstructured Mesh Applications

Carlo Bertolli, Adam Betts, Gihan R. Mudalige, Paul H.J. Kelly, Michael B. Giles

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

October 13, 2012 by hgpu

GPU-Based Local-Dimming for Power Efficient Imaging

Chihao Xu, Michael Krause, Jens Kruger

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Tags: Algorithms, Computer science, CUDA, Image generation, nVidia, nVidia GeForce GTX 555 M

October 13, 2012 by hgpu

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Peng Di, Ding Ye, Yu Su, Yulei Sui, Jingling Xue

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C1060, Tesla C2050

October 13, 2012 by hgpu

Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations

Max Grossman, Sameer Kulkarni

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Tags: Algorithms, ATI, Computer science, Heterogeneous systems, Machine learning, OpenCL, Optimization

October 9, 2012 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

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Mean shift for graph bundling

An implicit Tensor-Mass solver on the GPU for soft bodies simulation

AeminiumGPU: An Intelligent Framework for GPU Programming

.NET High Performance Computing

Parallel H-Tree Based Data Cubing on Graphics Processors

Programming NVIDIA cards by means of transitive closure based parallelization algorithms

Extendable Pattern-Oriented Optimization Directives (extended version)

Mesh Independent Loop Fusion for Unstructured Mesh Applications

GPU-Based Local-Dimming for Power Efficient Imaging

Automatic Parallelization of Tiled Loop Nests with Enhanced Fine-Grained Parallelism on GPUs

Applying Genetic Algorithms to Tune Heterogeneous Platform Configurations

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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