Applications

hgpu.org » Applications

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

M. J. Harvey, G. Giupponi, G. De Fabritiis

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Tags: Biological Physics, Chemistry, Computational Physics, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

Monte Carlo simulations on Graphics Processing Units

Vadim Demchik, Alexei Strelchenko

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Tags: ATI, ATI IL, ATI Radeon HD 4850, ATI Radeon HD 4870, ATI Stream, High Energy Physics - Lattice, Monte Carlo simulation, Physics

October 27, 2010 by hgpu

GPU Cluster for High Performance Computing

Zhe Fan, Feng Qiu, A. Kaufman, S. Yoakum-Stover

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Tags: Computer science, Fluid dynamics, GPU cluster, Lattice Boltzmann model, nVidia, nVidia GeForce FX 5800 Ultra

October 27, 2010 by hgpu

GPU Gems 3

Hubert Nguyen

Tags: Book, Computer science, nVidia, Programming techniques

October 27, 2010 by hgpu

Fast fluid dynamics simulation on the GPU

Mark Harris

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Tags: Cg, Computer science, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, Package, Programming techniques

October 27, 2010 by hgpu

Simulating spin models on GPU

Martin Weigel

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Tags: CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Statistical Mechanics, Tesla C1060

October 27, 2010 by hgpu

Getting Started with GPU Programming

Michael A. Gray

Tags: Computer science, Macintosh, nVidia, Tutorial

October 27, 2010 by hgpu

GPU implementation of JPEG XR

Ming C. Che, Jie Liang

Tags: Algorithms, Computer science, CUDA, Image processing, nVidia

October 27, 2010 by hgpu

Monte Carlo integration on GPU

J. Kanzaki

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Monte Carlo integration, nVidia, nVidia GeForce GTX 285, Package, Physics

October 27, 2010 by hgpu

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

N. F. Bate, C. J. Fluke, B. R. Barsdell, H. Garsden, G. F. Lewis

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Tags: Astrophysics, CUDA, Gravitational lensing, Instrumentation and Methods for Astrophysics, nVidia, Tesla S1070

October 27, 2010 by hgpu

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

Benjamin Block,Peter Virnau,Tobias Preis

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Tags: CUDA, GPU cluster, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi, Sam S. Stone, David B. Kirk, Wen-mei W. Hwu

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Performance, Programming techniques

October 27, 2010 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

Monte Carlo simulations on Graphics Processing Units

GPU Cluster for High Performance Computing

GPU Gems 3

Fast fluid dynamics simulation on the GPU

Simulating spin models on GPU

Getting Started with GPU Programming

GPU implementation of JPEG XR

Monte Carlo integration on GPU

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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