high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

Parallel Compression Checkpointing for Socket-Level Heterogeneous Systems

Yongpeng Liu, Hong Zhu, Yongyan Liu, Feng Wang, Baohua Fan

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Tags: Algorithms, ATI, ATI Radeon HD 4870, Compression, Computer science, Heterogeneous systems, OpenCL

October 22, 2011 by hgpu

Parallelization of the distinct lattice spring model

Gao-Feng Zhao, Jiannong Fang, Liang Sun, Jian Zhao

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Tags: Algorithms, Earth and Space Sciences, Geoscience, MPI, OpenMP, PC cluster

October 22, 2011 by hgpu

Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

Meilian Xu, Parimala Thulasiraman

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Tags: Algorithms, Cell processor, Heterogeneous systems, Image processing, Image recognition, Medicine, Optimization, Tomography

October 22, 2011 by hgpu

Concurrent Algorithms and Data Structures for Many-Core Processors

Daniel Cederman

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Performance, Sorting, Thesis

October 21, 2011 by hgpu

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Przemyslaw Stpiczynski

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Tags: Algorithms, BLAS, Computer science, CUDA, Hybrid computing, Linear Algebra, nVidia, Tesla C2050

October 21, 2011 by hgpu

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

Deian Stefan

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Optimization, Security, Stream Ciphers, Thesis

October 21, 2011 by hgpu

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

Jeyarajan Thiyagalingam, Daniel Goodman, Julia A. Schnabel, Anne Trefethen, Vicente Grau

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Tags: Algorithms, CUDA, Databases, Image processing, Medicine, nVidia, nVidia GeForce GTX 480, Tesla C2070

October 21, 2011 by hgpu

Computational Fluid Dynamics Using Graphics Processing Units: Challenges and Opportunities

S. Pratap Vanka, Aaron F. Shinn, Kirti C. Sahu

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, Review, Tesla C1060

October 21, 2011 by hgpu

DOPA: GPU-based protein alignment using database and memory access optimizations

Laiq Hasan, Marijn Kentie, Zaid Al-Ars

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Source codes

Tags: Algorithms, Bioinformatics, Biology, Computational Complexity, CUDA, Databases, nVidia, nVidia GeForce GTX 275, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

October 21, 2011 by hgpu

Efficient Synchronization Primitives for GPUs

Jeff A. Stuart, John D. Owens

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, Operating systems

October 21, 2011 by hgpu

GPU Accelerated X-Ray Image Enhancement

Mark James Brotherton

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Tags: Algorithms, DirectX, HLSL, Image processing, OpenMP, Review, Thesis

October 20, 2011 by hgpu

A high performance computing framework for physics-based modeling and simulation of military ground vehicles

Dan Negrut, David Lamb, David Gorsich

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Tags: Algorithms, Collision detection, CUDA, Distributed computing, Heterogeneous systems, nVidia, Physics, Tesla C1060

October 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

Parallel Compression Checkpointing for Socket-Level Heterogeneous Systems

Parallelization of the distinct lattice spring model

Mapping Iterative Medical Imaging Algorithm on Cell Accelerator

Concurrent Algorithms and Data Structures for Many-Core Processors

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Analysis and Implementation of eSTREAM and SHA-3 Cryptographic Algorithms

On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

Computational Fluid Dynamics Using Graphics Processing Units: Challenges and Opportunities

DOPA: GPU-based protein alignment using database and memory access optimizations

Efficient Synchronization Primitives for GPUs

GPU Accelerated X-Ray Image Enhancement

A high performance computing framework for physics-based modeling and simulation of military ground vehicles

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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