high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

Accelerating Braided B+ Tree Searches on a GPU with CUDA

Jordan Fix, Andrew Wilkes, Kevin Skadron

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480

December 7, 2011 by hgpu

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Petr Pospichal

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, nVidia, nVidia GeForce GTX 480, Performance

December 7, 2011 by hgpu

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Rohit Gupta, Martin B. van Gijzen, Kees Vuik

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Tags: Algorithms, Conjugate gradient solver, CUDA, Mathematics, nVidia, Tesla C2070

December 7, 2011 by hgpu

Multiprocessing Acceleration of H.264/AVC Motion Estimation Full Search Algorithm under CUDA Architecture

Eduarda R. Monteiro, Bruno B. Vizzotto, Claudio M. Diniz, Bruno Zatt, Sergio Bampi

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Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 480

December 6, 2011 by hgpu

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

GPU-Based Liquid Crystal Display Processing Platform

Song Peng

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Tags: Algorithms, CUDA, Image generation, Image processing, nVidia, nVidia Quadro FX 580, Thesis

December 6, 2011 by hgpu

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

Johannes Habich, Christian Feichtinger, Harald Kostler, Georg Hager, Gerhard Wellein

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Tags: Algorithms, ATI, ATI Radeon HD 6970, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Performance, Tesla C1060, Tesla C2070

December 6, 2011 by hgpu

A Fast 3D Spatial Analysis Technique Using Graphic Process Units

Ji Sang Park, Jong Min Lee, Seung Yeob Lee, Sung Woong Shin

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Tags: Algorithms, Earth and Space Sciences, nVidia, nVidia GeForce GTX 580, OpenCL, Raytracing

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

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

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

Accelerating Braided B+ Tree Searches on a GPU with CUDA

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Multiprocessing Acceleration of H.264/AVC Motion Estimation Full Search Algorithm under CUDA Architecture

DTAM: Dense tracking and mapping in real-time

Massively Parallel Identification of Intersection Points for GPGPU Ray Tracing

GPU-Based Liquid Crystal Display Processing Platform

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

A Fast 3D Spatial Analysis Technique Using Graphic Process Units

Soft Error Resilient QR Factorization for Hybrid System with GPGPU

Real-Time Handling of GPU Interrupts in LITMUS RT

Scalable Query Evaluation in Relational Databases

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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