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hgpu.org » Applications » Computer science

Computing Strongly Connected Components in Parallel on CUDA

Jiri Barnat, Petr Bauch, Lubos Brim, Milan Ceska

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 480

December 4, 2011 by hgpu

Multi-GPU Load Balancing for In-situ Visualization

R. Hagan, Y. Cao

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Tags: Computer science, CUDA, GPU cluster, N-body simulation, nVidia, nVidia GeForce GTX 295, Raytracing, Rendering, Visualization

December 4, 2011 by hgpu

Programming hybrid systems with implicit memory based synchronization

Jens Breitbart

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Tags: Algorithms, Computer science, CUDA, Hybrid computing, nVidia, nVidia GeForce GTX 480, Package, Tesla C2050

December 3, 2011 by hgpu

A Multi-View Stereo Implementation on Massively Parallel Hardware

Mate Beljan, Ronny Klowsky, Michael Goesele

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Rendering, Tesla C870

December 3, 2011 by hgpu

Design and Optimization of Hybrid MD5-Blowfish Encryption on GPUs

Zhu Wang, Josh Graham, Noura Ajam, Hai Jiang

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 6000, Optimization, Security, Tesla C2070

December 3, 2011 by hgpu

Effective GPU Strategies for LU Decomposition

Dinesh Bandara, Nalin Ranasinghe

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Tags: Algorithms, Computer science, CUDA, Matrix decomposition, nVidia, Tesla C1060

December 2, 2011 by hgpu

An error correction solver for linear systems: Evaluation of mixed precision implementations

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker

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Tags: Benchmarking, Computer science, CUDA, Mixed precision, nVidia, Tesla S1070

December 2, 2011 by hgpu

Auto-optimization of a Feature Selection Algorithm

Didem Unat, Han Suk Kim, Jurgen P. Schulze, Scott B. Baden

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Optimization, Rendering, Tesla C1060, Visualization

December 2, 2011 by hgpu

Evaluation of Fermi Features for Data Mining Algorithms

Sinduja Muralidharan

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Tags: Algorithms, Clustering, Computer science, CUDA, Data mining, Nearest neighbour, nVidia, Tesla C1060, Tesla C2050, Thesis

December 2, 2011 by hgpu

A programming language interface to describe transformations and code generation

Gabe Rudy, Malik Murtaza Khan, Mary Hall, Chun Chen, Jacqueline Chame

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Tags: Algorithms, Code generation, Computer science, CUBLAS, CUDA, MPI, nVidia, nVidia GeForce GTX 280, Tesla C2050

December 1, 2011 by hgpu

GPU Acceleration of Solving Parabolic Partial Differential Equations Using Difference Equations

David L. Foster

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Tags: Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 480, Optimization, Partial differential equations, PDEs, Tesla C1060

December 1, 2011 by hgpu

Scalable Data Clustering using GPU Clusters

Andrew Pangborn, Gregor von Laszewski, James Cavenaugh, Muhammad Shaaban, Roy Melton

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Tags: Algorithms, Cloud, Clustering, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX 260, OpenMP, Tesla C1060

December 1, 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

Computing Strongly Connected Components in Parallel on CUDA

Multi-GPU Load Balancing for In-situ Visualization

Programming hybrid systems with implicit memory based synchronization

A Multi-View Stereo Implementation on Massively Parallel Hardware

Design and Optimization of Hybrid MD5-Blowfish Encryption on GPUs

Effective GPU Strategies for LU Decomposition

An error correction solver for linear systems: Evaluation of mixed precision implementations

Auto-optimization of a Feature Selection Algorithm

Evaluation of Fermi Features for Data Mining Algorithms

A programming language interface to describe transformations and code generation

GPU Acceleration of Solving Parabolic Partial Differential Equations Using Difference Equations

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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