high performance computing on graphics processing units: hgpu.org

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Performance Portability with the Chapel Language

Albert Sidelnik, Saeed Maleki, Bradford L. Chamberlain, Maria J. Garzaran, David Padua

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

February 18, 2012 by hgpu

Proposition for propagated occupation grids for non-rigid moving objects tracking

Benjamin Lefaudeux, Gwennael Gate, Fawzi Nashashibi

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia

February 17, 2012 by hgpu

GPUs as Storage System Accelerators

Samer Al-Kiswany, Abdullah Gharaibeh, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, Security, Storage system, Tesla C2050

February 17, 2012 by hgpu

Interactive Manycore Photon Mapping

Bartosz Fabianowski

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Tags: Algorithms, Computer science, CUDA, KD-tree, Nearest neighbour, nVidia, nVidia GeForce GTX 280, Rendering, Thesis

February 17, 2012 by hgpu

A stereoscopic movie player with real-time content adaptation to the display geometry

Sylvain Duchene, Martin Lambers, Frederic Devernay

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Tags: Computer science, Image processing, nVidia, nVidia GeForce GTX 480, OpenGL, Package, Real-time graphics

February 17, 2012 by hgpu

An Efficient Implementation of Double Precision 1-D FFT for GPUs Using CUDA

Yanjun Liu, Licai Guo, Bin Luo, Xingyi Zhang

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 260, Programming techniques

February 17, 2012 by hgpu

Fast GPU-based image warping and inpainting for frame interpolation

Jakub Rosner, Hannes Fassold, Peter Schallauer, Werner Bailer

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Tags: Computer vision, CUDA, GPU, Image alignment, Image Inpainting, Image Warping, nVidia, nVidia GeForce GTX 285

February 17, 2012 by HannesF99

Comparing Many-Core Accelerator Frameworks

G. Haase, A. Kucher

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Optimization

February 15, 2012 by hgpu

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Andres Tomas, Chia-Chen Chang, Richard Scalettar, Zhaojun Bai

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Tags: Computer science, CUDA, Linear Algebra, Monte Carlo simulation, nVidia, QMC, Tesla C2050

February 15, 2012 by hgpu

Reflective Shadow Map Clustering for Real-Time Global Illumination

Roman Prutkin, Anton Kaplanyan, Carsten Dachsbacher

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Tags: 3D Graphics and Realism, Clustering, Computer science, DirectX, Image generation, nVidia, nVidia GeForce GTX 560 Ti

February 15, 2012 by hgpu

Graph Coarsening and Clustering on the GPU

B. O. Fagginger Auer, R. H. Bisseling

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Tags: Algorithms, Clustering, Computer science, CUDA, Graph theory, nVidia, Package, Tesla C2050

February 15, 2012 by hgpu

Robust Real-Time Multiprocessor Interrupt Handling Motivated by GPUs

Glenn A. Elliott, James H. Anderson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 470, Package

February 14, 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

Performance Portability with the Chapel Language

Proposition for propagated occupation grids for non-rigid moving objects tracking

GPUs as Storage System Accelerators

Interactive Manycore Photon Mapping

A stereoscopic movie player with real-time content adaptation to the display geometry

An Efficient Implementation of Double Precision 1-D FFT for GPUs Using CUDA

Fast GPU-based image warping and inpainting for frame interpolation

Comparing Many-Core Accelerator Frameworks

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Reflective Shadow Map Clustering for Real-Time Global Illumination

Graph Coarsening and Clustering on the GPU

Robust Real-Time Multiprocessor Interrupt Handling Motivated by 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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