high performance computing on graphics processing units: hgpu.org

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Parallel Genetic Programming on Graphics Processing Units

Douglas A. Augusto, Heder S. Bernardino, Helio J.C. Barbosa

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Tags: Algorithms, Computer science, Genetic programming, OpenCL, Optimization, Review

October 31, 2012 by hgpu

Impact of GPU Memory Access Patterns on FDTD

Matthew Livesey, James F. Stack Jr., Fumie Costenz, Takeshi Nanri, Norimasa Nakashima, Seiji Fujino

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Tags: Computer science, CUDA, FDTD, Finite-difference time-domain, nVidia, Performance, Tesla M2050

October 30, 2012 by hgpu

Characterization and Exploitation of GPU Memory Systems

Kenneth Sydney Lee

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Tags: AMD Fusion, ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computer science, Memory model, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla C2075, Thesis

October 30, 2012 by hgpu

A GPU Approach to Fortran Legacy Systems

Mariano Mendez, Fernando G. Tinetti

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Tags: Computer science, CUDA, Fortran, nVidia, OpenACC, Programming techniques, Tesla C2075

October 29, 2012 by hgpu

Silhouette Extraction using Graphics Processing Units

Kristian Haga Karstensen

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Tags: Algorithms, Computer vision, Image processing, nVidia, nVidia GeForce GTX 460, OpenCL, Optimization, Package, Thesis

October 29, 2012 by hgpu

Three dimensional tracking of gold nanoparticles using digital holographic microscopy

Frederic Verpillat, Fadwa Joud, Pierre Desbiolles, Michel Gross

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Tags: Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Optics, Pattern recognition, Physics

October 28, 2012 by hgpu

Historygrams: Enabling Interactive Global Illumination in Direct Volume Rendering using Photon Mapping

Daniel Jonsson, Joel Kronander, Timo Ropinski, Anders Ynnerman

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 570, Rendering

October 28, 2012 by hgpu

Analysis & Design of Efficient Cryptographic Systems

Michael Lehmann

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package, Security, Stream Ciphers

October 28, 2012 by hgpu

BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization

A.F. van Dixhoorn, J. Milles, B. van Lew, C.P. Botha

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Tags: Computer science, Filtering, Image generation, MRI, OpenCL, OpenGL, Package, Rendering, Visualization

October 26, 2012 by hgpu

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

Nicholas John Moore

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Tags: Computer science, CUDA, FPGA, Image reconstruction, nVidia, Optimization, Tesis, Tesla C1060, Tesla C2070

October 26, 2012 by hgpu

An Environment to Support GPU and Multicore Programming for Rapid, High Performance, Application Deployment

James Laurence Brock

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Heterogeneous systems, Image reconstruction, Monte Carlo simulation, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 560 Ti, OpenCL, Tesla C1060, Tesla S1070, Thesis

October 26, 2012 by hgpu

Automatic Generation Of Application-Specific Accelerators for FPGAs from Python Loop Nests

David Sheffield, Michael Anderson, Kurt Keutzer

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Tags: Computer science, Design space exploration, FPGA, Package, Python

October 25, 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

Parallel Genetic Programming on Graphics Processing Units

Impact of GPU Memory Access Patterns on FDTD

Characterization and Exploitation of GPU Memory Systems

A GPU Approach to Fortran Legacy Systems

Silhouette Extraction using Graphics Processing Units

Three dimensional tracking of gold nanoparticles using digital holographic microscopy

Historygrams: Enabling Interactive Global Illumination in Direct Volume Rendering using Photon Mapping

Analysis & Design of Efficient Cryptographic Systems

BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

An Environment to Support GPU and Multicore Programming for Rapid, High Performance, Application Deployment

Automatic Generation Of Application-Specific Accelerators for FPGAs from Python Loop Nests

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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