high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Deep Shadow Maps from Volumetric Data on the GPU

Adam J. Shook

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Tags: Computer science, DirectX, HLSL, nVidia, nVidia GeForce GTX 465, Raytracing

December 30, 2011 by hgpu

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

George C. Caragea, Alexandros Tzannes, Fuat Keceli, Rajeev Barua, Uzi Vishkin

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Source codes

Tags: Algorithms, ASIC, Benchmarking, Computer science, Memory level parallelism, nVidia, Package, Prefetch

December 29, 2011 by hgpu

Scalable Fast Multipole Methods on Distributed Heterogeneous Architectures

Qi Hu, Nail A. Gumerov, Ramani Duraiswami

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce GTX 480, Tesla C2050, Tesla S1070

December 29, 2011 by hgpu

Visualization assisted by parallel processing

B. Lange, W. Puech, and N. Rodriguez, H. Rey, X. Vasques

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Tags: Algorithms, Benchmarking, Computer science, nVidia, nVidia GeForce GT 330, OpenCL, Rendering, Visualization, Voronoi diagram

December 29, 2011 by hgpu

Engineering Concurrent Software Guided by Statistical Performance Analysis

Clemens Grelck, Kevin Hammond, Heinz Hertlein, Philip Holzenspies, Chris Jesshope, Raimund Kirner, Bernd Scheuermann, Alex Shafarenko, Iraneus Te Boekhorst, Volkmar Wieser

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 425 M, Performance, Visualization

December 29, 2011 by hgpu

Improving the speed of neural networks on CPUs

Vincent Vanhoucke, Andrew Senior, Mark Z. Mao

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Tags: Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla C2070, Tutorial

December 28, 2011 by hgpu

Multilevel Tile Load Map on Massive Terrain Visualization

Junlan Nie, Dongliang Guo, Yanfen Wang, Lingfu Kong, Yong Tang

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Tags: Algorithms, Computer science, GLSL, Image processing, nVidia, nVidia GeForce GT 240 M, OpenGL, Optimization, Rendering, Visualization

December 28, 2011 by hgpu

GPU-Based Global Illumination Using Lightcuts

Tong Zhang

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, Tesla C1060, Thesis

December 28, 2011 by hgpu

A Fast Algorithm for Constructing Inverted Files on Heterogeneous Platforms

Zheng Wei, Joseph JaJa

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Heterogeneous systems, nVidia, Tesla C1060

December 28, 2011 by hgpu

Monitoring Multiple Streams with Dynamic Time Warping using Graphic Processors

Jason Chang, Mi-Yen Yeh

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, Performance, Tesla C1060

December 28, 2011 by hgpu

Efficient parallel lists intersection and index compression algorithms using graphics processing units

Naiyong Ao, Fan Zhang, Di Wu, Douglas S. Stones, Gang Wang, Xiaoguang Liu, Jing Liu, Sheng Lin

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Tags: Algorithms, Compression, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 480, Performance

December 28, 2011 by hgpu

Real-Time Rendering of Temporal Volumetric Data on a GPU

Biao She, Pierre Boulanger, Michelle Noga

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Tags: Cg, Computer science, nVidia, nVidia Quadro FX 5800, Rendering

December 28, 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

Applications

Deep Shadow Maps from Volumetric Data on the GPU

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Scalable Fast Multipole Methods on Distributed Heterogeneous Architectures

Visualization assisted by parallel processing

Engineering Concurrent Software Guided by Statistical Performance Analysis

Improving the speed of neural networks on CPUs

Multilevel Tile Load Map on Massive Terrain Visualization

GPU-Based Global Illumination Using Lightcuts

A Fast Algorithm for Constructing Inverted Files on Heterogeneous Platforms

Monitoring Multiple Streams with Dynamic Time Warping using Graphic Processors

Efficient parallel lists intersection and index compression algorithms using graphics processing units

Real-Time Rendering of Temporal Volumetric Data on a GPU

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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