high performance computing on graphics processing units: hgpu.org

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Particle method on GPU

Jean-Matthieu Etancelin, Georges-Henri Cottet, Christophe Picard, Franck Perignon

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Tags: Algorithms, ATI, ATI Radeon HD 6770 M, Computer science, OpenCL, Python

January 31, 2013 by hgpu

Many-threaded Differential Evolution on the GPU

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham

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Tags: Algorithms, Computer science, CUDA, Differential evolution, Metaheuristics, nVidia, Tesla C2050

January 30, 2013 by hgpu

Scheduling (ir)regular applications on heterogeneous platforms

Artur Miguel Matos Mariano

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Tags: Computer science, CUDA, FFT, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce GTX 580, Task scheduling, Thesis

January 30, 2013 by hgpu

GPUDet: A Deterministic GPU Architecture

Hadi Jooybar, Wilson W. L. Fung, Mike O'Connor, Joseph Devietti, Tor M. Aamodt

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Tags: Computer science, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce GTX 480

January 29, 2013 by hgpu

A dataflow-like programming model for future hybrid clusters

Jens Breitbart

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Tags: Cluster computing, Computer science, CUDA, Distributed data structures, Hybrid computing, nVidia, nVidia GeForce GTX 480, Tesla C2050

January 28, 2013 by hgpu

Exploring Different Automata Representations for Efficient Regular Expression Matching on GPUs

Xiaodong Yu, Michela Becchi

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Tags: Cellular automata, Computer science, CUDA, nVidia, Performance, Security

January 28, 2013 by hgpu

Warped Register File: A Power Efficient Register File for GPGPUs

Mohammad Abdel-Majeed, Murali Annavaram

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, Performance

January 28, 2013 by hgpu

GPUfs: Integrating a File System with GPUs

Mark Silberstein, Bryan Ford, Idit Keidar, Emmett Witchel

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Tags: Computer science, CUDA, nVidia, Operating systems, Tesla C2075

January 26, 2013 by hgpu

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Ishan Rajani, G Nanda Gopal

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Tags: AMD FireStream 9270, ATI, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Warp level parallelism

January 26, 2013 by hgpu

Selection algorithm of graphic accelerators in heterogeneous cluster for optimization computing

A. M. Lavreniuk, M. S. Lavreniuk

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, OpenCL, Tesla M2050

January 26, 2013 by hgpu

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Malik Muhammad Zaki Murtaza Khan

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Tags: Code generation, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

January 26, 2013 by hgpu

Automatic Parallelization for GPUs

Thomas B. Jablin

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques, Thesis

January 26, 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

Applications

Particle method on GPU

Many-threaded Differential Evolution on the GPU

Scheduling (ir)regular applications on heterogeneous platforms

GPUDet: A Deterministic GPU Architecture

A dataflow-like programming model for future hybrid clusters

Exploring Different Automata Representations for Efficient Regular Expression Matching on GPUs

Warped Register File: A Power Efficient Register File for GPGPUs

GPUfs: Integrating a File System with GPUs

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Selection algorithm of graphic accelerators in heterogeneous cluster for optimization computing

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Automatic Parallelization for GPUs

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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