high performance computing on graphics processing units: hgpu.org

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CUDA Fortran for Scientists and Engineers

Greg Ruetsch, Massimiliano Fatica

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Tags: Book, Computer science, CUDA, Fortran, High-level Languages, nVidia, Optimization, Tutorial

January 26, 2012 by hgpu

A Parallel Ant Colony Optimization Algorithm for the Travelling Salesman Problem: Improving Performance Using CUDA

Octavian Nitica

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Tags: Algorithms, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Tesla C1060, Tesla C2050, Tesla C870, Thesis

January 26, 2012 by hgpu

Using a GPU, Online Diarization – Offline Diarization

Gerald Friedland

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Tags: Acoustics, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Speech recognition

January 26, 2012 by hgpu

Parallel Symbolic Analysis of Large Analog Circuits on GPU Platforms

Sheldon X.-D. Tan, Xue-Xin Liu, Eric Mlinar and Esteban Tlelo-Cuautle

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Tags: Computer science, CUDA, Electronic design automation, nVidia, Tesla S1070

January 26, 2012 by hgpu

Scalable Parallel Minimum Spanning Forest Computation

Sadegh Nobari, Thanh-Tung Cao, Panagiotis Karras, Stephane Bressan

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla S1070

January 25, 2012 by hgpu

Parallel LDPC Decoder Implementation on GPU Based on Unbalanced Memory Coalescing

Soonyoung Kang, Jaekyun Moon

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Parity-check codes

January 25, 2012 by hgpu

Multifrontal Sparse Matrix Factorization on Graphics Processing Units

Robert F. Lucas, Gene Wagenbreth, John J. Tran, Dan M. Davis

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Tags: Computer science, CUDA, Factorization, nVidia, nVidia GeForce 8800, Sparse matrix

January 25, 2012 by hgpu

TAP: A TLP-Aware Cache Management Policy for a CPU-GPU Heterogeneous Architecture

Jaekyu Lee, Hyesoon Kim

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Tags: Computer science, Heterogeneous systems, Memory, Performance

January 25, 2012 by hgpu

Parallel Algorithm Design and Implementation of Regular/Irregular Problems: An In-depth Performance Study on Graphics Processing Units

Steven Solomon

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, Optimization, Thesis

January 25, 2012 by hgpu

Realtime scheduling using GPUs – proof of feasibility

Peter Fodrek, Ludovit Farkas, Tomas Murgas

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GT 310 M, OpenCL, Task scheduling

January 25, 2012 by hgpu

GPU algorithms for comparison-based sorting and for merging based on multiway selection

Nikolaj Leischner

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, OpenCL, Sorting, Thesis

January 25, 2012 by hgpu

The GPU Enhanced Parallel Computing for Large Scale Data Clustering

Xiaohui Cui, Jesse St. Charles, Justin Beaver, Thomas E. Potok

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

January 24, 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

CUDA Fortran for Scientists and Engineers

A Parallel Ant Colony Optimization Algorithm for the Travelling Salesman Problem: Improving Performance Using CUDA

Using a GPU, Online Diarization – Offline Diarization

Parallel Symbolic Analysis of Large Analog Circuits on GPU Platforms

Scalable Parallel Minimum Spanning Forest Computation

Parallel LDPC Decoder Implementation on GPU Based on Unbalanced Memory Coalescing

Multifrontal Sparse Matrix Factorization on Graphics Processing Units

TAP: A TLP-Aware Cache Management Policy for a CPU-GPU Heterogeneous Architecture

Parallel Algorithm Design and Implementation of Regular/Irregular Problems: An In-depth Performance Study on Graphics Processing Units

Realtime scheduling using GPUs – proof of feasibility

GPU algorithms for comparison-based sorting and for merging based on multiway selection

The GPU Enhanced Parallel Computing for Large Scale Data Clustering

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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