high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Online Adaptive Code Generation and Tuning

Ananta Tiwari, Jeffrey K. Hollingsworth

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Tags: Code generation, Computer science, Optimization

November 21, 2011 by hgpu

Issues in Heterogenenous GPU Clusters

Patrick Carpenter, William Symon

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Tags: Computer science, GPU cluster, Heterogeneous systems, Review

November 21, 2011 by hgpu

A new approach for sparse matrix vector product on NVIDIA GPUs

F. Vazquez, J. J. Fernandez, E. M. Garzon

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Sparse matrix

November 21, 2011 by hgpu

Efficient GPGPU-based parallel packet classification

Che-Lun Hung, Yaw-Ling Lin, Kuan-Ching Li, Hsiao-Hsi Wang, Shih-Wei Guo

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Tags: Algorithms, Computer science, CUDA, Filtering, Network communications, nVidia, nVidia GeForce GTS 450, Security

November 21, 2011 by hgpu

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

Eugene Burmako, Rauf Sadykhov

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Tags: Algorithms, Computer science, CUDA, High-level Languages, nVidia, Package

November 21, 2011 by hgpu

Graph-based Parallel Analysis of Large Analog Circuits Based on GPU Platforms

Jianan Lu, Zhigang Hao, Sheldon X.-D. Tan

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Tags: Algorithms, CAD, Computer science, CUDA, Electronic design automation, Graph theory, nVidia, Tesla S1070

November 21, 2011 by hgpu

Challenge benchmarks that must be conquered to sustain the gpu revolution

Emily Blem, Matthew Sinclair, Karthikeyan Sankaralingam

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Tags: Benchmarking, Computer science, CUDA, nVidia, Tesla C1060

November 21, 2011 by hgpu

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, Java, nVidia, Optimization, Package, Tesla C2050

November 21, 2011 by hgpu

Efficient Stack-less BVH Traversal for Ray Tracing

Michal Hapala, Tomas Davidovic, Ingo Wald, Vlastimil Havran, Philipp Slusallek

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 260, nVidia GeForce GTX 470, Raytracing

November 20, 2011 by hgpu

Implementing a Finite Difference-Based Real-time Sound Synthesizer using GPUs

Marc Sosnick, William Hsu

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Tags: Acoustics, Algorithms, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GT 330 M, Package, Physics

November 20, 2011 by hgpu

Soft Error Resilient QR Factorization for Hybrid System

Peng Du, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Fault tolerance, Hybrid computing, Linear Algebra, nVidia, Tesla C2050, Tesla M2070

November 20, 2011 by hgpu

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

Kwang-Ting (Tim) Cheng, Yi-Chu Wang

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Tags: ARM, Computer science, Computer vision, FFT, Image recognition, nVidia, nVidia Tegra

November 20, 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

Online Adaptive Code Generation and Tuning

Issues in Heterogenenous GPU Clusters

A new approach for sparse matrix vector product on NVIDIA GPUs

Efficient GPGPU-based parallel packet classification

Conflux: Embedding Massively Parallel Semantics in a High-Level Programming Language

Graph-based Parallel Analysis of Large Analog Circuits Based on GPU Platforms

Challenge benchmarks that must be conquered to sustain the gpu revolution

PATUS: A Code Generation and Autotuning Framework For Parallel Iterative Stencil Computations on Modern Microarchitectures

Efficient Stack-less BVH Traversal for Ray Tracing

Implementing a Finite Difference-Based Real-time Sound Synthesizer using GPUs

Soft Error Resilient QR Factorization for Hybrid System

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

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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