high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Full system simulation of many-core heterogeneous SoCs using GPU and QEMU semihosting

Shivani Raghav, Christian Pinto, Martino Ruggiero, Andrea Marongiu, David Atienza, Luca Benini

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Operating systems, Software Engineering, Tesla C2070

March 26, 2012 by hgpu

Impact of asynchronism on GPU accelerated parallel iterative computations

Sylvain Contassot-Vivier, Thomas Jost, Stephane Vialle

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Partial differential equations, PDEs, Performance

March 22, 2012 by hgpu

Hierarchical N-body simulations with auto-tuning for heterogeneous systems

Rio Yokota, Lorena A. Barba

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Tags: Algorithms, CUDA, Heterogeneous systems, Mathematical Software, N-body simulation, Numerical Analysis, nVidia, nVidia GeForce GTX 590, Physics, Tesla C2050

March 22, 2012 by hgpu

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Carla Ramiro Sanchez, Jose J. Lopez-Espin, Domingo Gimenez, Antonio M. Vidal

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2070

March 21, 2012 by hgpu

CPU-GPU Hybrid Parallel Binomial American Option Pricing

Nan Zhang, Eng Gee Lim, Ka Lok Man, Chi-Un Lei

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Tags: Algorithms, CUDA, Finance, Heterogeneous systems, Hybrid computing, nVidia, nVidia Quadro NVS 160 M

March 21, 2012 by hgpu

Targeting heterogeneous architectures via macro data flow

M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Torquati

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, Tesla C2050

March 13, 2012 by hgpu

Real-time execution of image change detection

Hugo van Lint

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Tags: Algorithms, CUDA, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 480, Thesis

March 13, 2012 by hgpu

Dynamic Compilation of Data-Parallel Kernels for Vector Processors

Andrew Kerr, Gregory Diamos, Sudhakar Yalamanchili

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia

March 12, 2012 by hgpu

NUMA Data-Access Bandwidth Characterization and Modeling

Ryan Karl Braithwaite

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla C2050, Tesla M2050, Thesis

March 11, 2012 by hgpu

Asynchronous Parallel Computing Model of Global Motion Estimation with CUDA

Zuo Chen, Jialiang Ji, Renfa Li

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 220, Video encoding

March 9, 2012 by hgpu

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Fahian Ahmed, Saddam Quirem, Bum Joo Shin, Duk Joo Son, Young Choon Woo, Byeong Kil Lee, Wan Choi

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Tags: Algorithms, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, Optimization, Security, Tesla C1060

March 9, 2012 by hgpu

Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters

Jonathan Lifflander, G. Carl Evans, Anshu Arya, Laxmikant V. Kale

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Tags: Computer science, CUDA, Heterogeneous systems, N-body simulation, nVidia, Particle simulation, Tesla S1070

March 9, 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

Full system simulation of many-core heterogeneous SoCs using GPU and QEMU semihosting

Impact of asynchronism on GPU accelerated parallel iterative computations

Hierarchical N-body simulations with auto-tuning for heterogeneous systems

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

CPU-GPU Hybrid Parallel Binomial American Option Pricing

Targeting heterogeneous architectures via macro data flow

Real-time execution of image change detection

Dynamic Compilation of Data-Parallel Kernels for Vector Processors

NUMA Data-Access Bandwidth Characterization and Modeling

Asynchronous Parallel Computing Model of Global Motion Estimation with CUDA

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters

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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