high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Distributed time, conservative parallel logic simulation on GPUs

Bo Wang, Yuhao Zhu, Yangdong Deng

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Tags: Computer science, CUDA, Electronic design automation, Hardware Architecture, nVidia, nVidia GeForce GTX 280, Performance

May 31, 2011 by hgpu

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

K. Dohi, K. Benkridt, C. Ling, T. Hamada, Y. Shibata

Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Smith-Waterman algorithm

May 31, 2011 by hgpu

Connected component identification and cluster update on GPU

Martin Weigel

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Ising model, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Physics, Statistical Mechanics, Tesla M2070

May 31, 2011 by hgpu

Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

Weihang Zhu, J. Curry

Tags: Algorithms, Computer science, Nonlinear optimization, Optimization, Pattern Search, Performance

May 30, 2011 by hgpu

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

Christoph Muller, Steffen Frey, Magnus Strengert, Carsten Dachsbacher, Thomas Ertl

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 5600, Performance

May 30, 2011 by hgpu

A Micro-benchmark Suite for AMD GPUs

Ryan Taylor, Xiaoming Li

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Tags: ATI, ATI IL, ATI Stream, Benchmarking, Computer science, Presentation, Review, RV770, RV870

May 30, 2011 by hgpu

Real-Time Rendering and Manipulation of Large Terrains

Shiben Bhattacharjee, Suryakant Patidar, P. J. Narayanan

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Tags: Computer science, GLSL, nVidia, nVidia GeForce 8800 GTX, OpenGL, Real-time graphics, Rendering

May 30, 2011 by hgpu

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

Alhadi Bustamam, Kevin Burrage, Nicholas A. Hamilton

Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, Sparse matrix

May 30, 2011 by hgpu

Evaluating the potential of graphics processors for high performance embedded computing

Shuai Mu, Chenxi Wang, Ming Liu, Dongdong Li, Maohua Zhu, Xiaoliang Chen, Xiang Xie, Yangdong Deng

Tags: ASIC, Benchmarking, Computer science, DSP, Embedded high-performance computing

May 30, 2011 by hgpu

High performance comparison-based sorting algorithm on many-core GPUs

Xiaochun Ye, Dongrui Fan, Wei Lin, Nan Yuan, Paolo Ienne

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Performance, Sorting

May 30, 2011 by hgpu

VHF SAR image formation implemented on a GPU

M. Blom, P. Follo

Tags: Earth and Space Sciences, FFT, Geoscience

May 30, 2011 by hgpu

GPU-Accelerated Background Generation Algorithm with Low Latency

Julius Fabian Ohmer, Peter G. Perry, Nicholas J. Redding

Tags: Algorithms, Image processing, Image registration, OpenGL

May 30, 2011 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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high performance computing on graphics processing units: hgpu.org

Applications

Distributed time, conservative parallel logic simulation on GPUs

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

Connected component identification and cluster update on GPU

Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

A Micro-benchmark Suite for AMD GPUs

Real-Time Rendering and Manipulation of Large Terrains

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

Evaluating the potential of graphics processors for high performance embedded computing

High performance comparison-based sorting algorithm on many-core GPUs

VHF SAR image formation implemented on a GPU

GPU-Accelerated Background Generation Algorithm with Low Latency

Recent source codes

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

Most viewed papers (last 30 days)