high performance computing on graphics processing units: hgpu.org

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CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

Daniel N. Benedetti, W. Randolph Franklin, Wenli Li

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Tags: Algorithms, Compression, CUDA, Differential equations, Image processing, nVidia, Partial differential equations, PDEs, Thrust

October 10, 2013 by hgpu

GALAMOST: GPU-accelerated large-scale molecular simulation toolkit

You-Liang Zhu, Hong Liu, Zhan-Wei Li, Hu-Jun Qian, Giuseppe Milano, Zhong-Yuan Lu

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Tags: Chemical Physics, Computational Physics, CUDA, Molecular simulation, nVidia, nVidia GeForce GTX 580, Phase transition, Physics, Soft Condensed Matter, Tesla K20

October 10, 2013 by hgpu

Direct deconvolution of radio synthesis images using L1 minimisation

Stephen J. Hardy

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Tags: Astrophysics, CUDA, Image reconstruction, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla M2050

October 10, 2013 by hgpu

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Blesson Varghese, Andrew Rau-Chaplin

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Tags: Computational Engineering, Computer science, CUDA, Finance, nVidia, Risk Management, Tesla C2075

October 10, 2013 by hgpu

Scalable Fast Multipole Methods on Heterogeneous Architecture

Q. Hu

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Tesla C1060, Tesla C2050, Thesis

October 9, 2013 by hgpu

Enabling the use of Heterogeneous Computing for Bioinformatics

Ramakrishna Bijanapalli Chakri

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Tags: Bioinformatics, Biology, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, Smith-Waterman algorithm, Thesis

October 8, 2013 by hgpu

Stressing the BER simulation of LDPC codes in the error floor region using GPU clusters

Gabriel Falcao, Joao Andrade, Vitor Silva, Shinichi Yamagiwa, Leonel Sousa

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Tags: Computer science, CUDA, GPU cluster, Monte Carlo simulation, nVidia, Parity-check codes, Tesla M2050

October 8, 2013 by hgpu

Parallel and Distributed Implementations of Multiple and Two-Dimensional Pattern Matching Algorithms

Charalampos S. Kouzinopoulos

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 280, String matching, Thesis

October 8, 2013 by hgpu

libcloudph++ 0.1: single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++

Sylwester Arabas, Anna Jaruga, Hanna Pawlowska, Wojciech W. Grabowski

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Tags: Algorithms, CUDA, Earth and Space Sciences, nVidia, nVidia Quadro 600, Package, Physics, Thrust

October 8, 2013 by hgpu

Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX

Tilman Dannert, Andreas Marek, Markus Rampp

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Tags: Computational Physics, CUDA, GPU cluster, Heterogeneous systems, nVidia, PC cluster, Physics, Plasma physics, Solar and Stellar Astrophysics, Tesla K20

October 8, 2013 by hgpu

Performance evaluation of CUDA programming for machining simulation

Felix Abecassis, Sylvain Lavernhe, Christophe Tournier, Pierre-Alain Boucard

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Tags: Computer science, CUDA, nVidia, nVidia Quadro 4000, Physics

October 7, 2013 by hgpu

GPU Accelerated Conjunction Assessment with Applications to Formation Flight and Space Debris Tracking

Abel Brown, Jason Tichy, Michael Demoret, David Rand

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Tags: CUDA, Earth and Space Sciences, nVidia, Tesla M2050

October 7, 2013 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

CUDA-Accelerated ODETLAP: A Parallel Lossy Compression Implementation

GALAMOST: GPU-accelerated large-scale molecular simulation toolkit

Direct deconvolution of radio synthesis images using L1 minimisation

Accounting for Secondary Uncertainty: Efficient Computation of Portfolio Risk Measures on Multi and Many Core Architectures

Scalable Fast Multipole Methods on Heterogeneous Architecture

Enabling the use of Heterogeneous Computing for Bioinformatics

Stressing the BER simulation of LDPC codes in the error floor region using GPU clusters

Parallel and Distributed Implementations of Multiple and Two-Dimensional Pattern Matching Algorithms

libcloudph++ 0.1: single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++

Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX

Performance evaluation of CUDA programming for machining simulation

GPU Accelerated Conjunction Assessment with Applications to Formation Flight and Space Debris Tracking

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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