high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Efficient Workload Balancing on Heterogeneous GPUs using Mixed-Integer Non-Linear Programming

Chih-Sheng Lin, Chih-Wei Hsieh, Hsi-Ya Chang, Pao-Ann Hsiung

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 690, Tesla C2050, Tesla K20

December 20, 2014 by hgpu

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Farouk Mansouri, Sylvain Huet, Dominique Houzet

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GT 610, nVidia GeForce GTX Titan, nVidia Quadro 4000, Signal processing

December 20, 2014 by hgpu

Analysis and Optimization Techniques for Massively Parallel Processors

Wenhao Jia

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, PTX, Tesla C2070, Thesis, Virtualization

December 15, 2014 by hgpu

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Jose M. Andion, Manuel Arenaz, Francois Bodin, Gabriel Rodriguez, Juan Tourino

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, Tesla S1070, Tesla S2050

December 14, 2014 by hgpu

MLitB: Machine Learning in the Browser

Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling

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Tags: Algorithms, Computer science, Distributed computing, Heterogeneous systems, Java, Machine learning, Neural networks, Package

December 9, 2014 by hgpu

Portable OpenCL Out-of-Order Execution Framework for Heterogeneous Platforms

Ville Korhonen

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Tags: Computer science, Data parallelism, Heterogeneous systems, OpenCL, Task scheduling, Thesis

December 9, 2014 by hgpu

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

David Abdurachmanov, Brian Bockelman, Peter Elmer, Giulio Eulisse, Robert Knight, Shahzad Muzaffar

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Tags: ARM, Computational Physics, Computer science, Distributed computing, Heterogeneous systems, High Energy Physics - Experiment, Intel Xeon Phi, Physics

December 5, 2014 by hgpu

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, Edgar Isaac Hiroshi Leon Saiki

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan, Package

December 2, 2014 by hgpu

Application Synthesis and Optimization on Heterogeneous Parallel Processing Systems

Chih-Sheng Lin

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 690, OpenCL, Task scheduling, Tesla C2050, Thesis

December 1, 2014 by hgpu

Automatic Performance Tuning of Pipeline Patterns for Heterogeneous Parallel Architectures

E. Bajrovic, S. Benkner

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, OpenCV, Performance, Tesla C2050, Tesla K20

November 20, 2014 by hgpu

Hybrid CPU-GPU Pipeline Framework

Fahad Khalid, Frank Feinbube, Andreas Polze

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Tags: Biology, Computational biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680

November 18, 2014 by hgpu

Semi-Analytic Solutions to the Radiative Transfer Equations via Hetergeneous Computing

Daniel Alphin Holladay

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Tags: Astrophysics, Heterogeneous systems, nVidia, OpenCL, Physics, Tesla C2050, Tesla C2075, Thesis

November 13, 2014 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

Efficient Workload Balancing on Heterogeneous GPUs using Mixed-Integer Non-Linear Programming

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Analysis and Optimization Techniques for Massively Parallel Processors

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

MLitB: Machine Learning in the Browser

Portable OpenCL Out-of-Order Execution Framework for Heterogeneous Platforms

Heterogeneous High Throughput Scientific Computing with APM X-Gene and Intel Xeon Phi

An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU

Application Synthesis and Optimization on Heterogeneous Parallel Processing Systems

Automatic Performance Tuning of Pipeline Patterns for Heterogeneous Parallel Architectures

Hybrid CPU-GPU Pipeline Framework

Semi-Analytic Solutions to the Radiative Transfer Equations via Hetergeneous Computing

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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