high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

DTAM: Dense tracking and mapping in real-time

Richard A. Newcombe, Steven J. Lovegrove, Andrew J. Davison

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Tags: Algorithms, Computer science, Computer vision, Image alignment, nVidia, nVidia GeForce GTX 480

December 6, 2011 by hgpu

Massively Parallel Identification of Intersection Points for GPGPU Ray Tracing

Alexandre S. Nery, Nadia Nedjah, Felipe M. G. Franca, Lech Jozwiak

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 465, Raytracing

December 6, 2011 by hgpu

Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Shubham Gupta, M.Rajasekhara Babu

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GT 210 M, OpenMP, Performance, String matching

December 6, 2011 by hgpu

Real-time Terrain Modeling using CPU-GPU Coupled Computation

Adrien Bernhardt, Andre Maximo, Luiz Velho, Houssam Hnaidi, Marie-Paule Cani

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Tags: Computer science, nVidia, nVidia GeForce GTS 480, OpenGL, Real-time graphics, Rendering, Tessellation

December 6, 2011 by hgpu

Soft Error Resilient QR Factorization for Hybrid System with GPGPU

Peng Du, Piotr Luszczek, Stan Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Hybrid computing, nVidia, Tesla C2050, Tesla M2070

December 5, 2011 by hgpu

Real-Time Handling of GPU Interrupts in LITMUS RT

Glenn A. Elliott, Chih-Hao Sun, James H. Anderson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, Operating systems

December 5, 2011 by hgpu

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Alecio Pedro Delazari Binotto

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Task scheduling, Thesis

December 5, 2011 by hgpu

Scalable Query Evaluation in Relational Databases

Rasmus Resen Amossen

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Tags: Algorithms, Computer science, Databases, Matrix multiplication, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization, Python, Thesis

December 5, 2011 by hgpu

GROPHECY: GPU performance projection from CPU code skeletons

Jiayuan Meng, Vitali A. Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas D. Uram

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Tags: Analytical model, Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, Performance, Tesla C1060

December 5, 2011 by hgpu

GPU-Based Parallel Multi-objective Particle Swarm Optimization

You Zhou, Ying Tan

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, Optimization

December 4, 2011 by hgpu

Communication-avoiding QR decomposition for GPUs

Michael Anderson, Grey Ballard, James Demmel, Kurt Keutzer

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Tags: Algorithms, Computer science, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX 480, Tesla C2050

December 4, 2011 by hgpu

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs

Fumihiko Ino, Yuma Munekawa, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, Distributed computing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Search, Smith-Waterman algorithm

December 4, 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

DTAM: Dense tracking and mapping in real-time

Massively Parallel Identification of Intersection Points for GPGPU Ray Tracing

Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Real-time Terrain Modeling using CPU-GPU Coupled Computation

Soft Error Resilient QR Factorization for Hybrid System with GPGPU

Real-Time Handling of GPU Interrupts in LITMUS RT

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Scalable Query Evaluation in Relational Databases

GROPHECY: GPU performance projection from CPU code skeletons

GPU-Based Parallel Multi-objective Particle Swarm Optimization

Communication-avoiding QR decomposition for GPUs

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle 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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