high performance computing on graphics processing units: hgpu.org

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Adapting a message-driven parallel application to GPU-accelerated clusters

James C. Phillips, John E. Stone, Klaus Schulten

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Tags: Computer science, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia Quadro FX 5600

October 28, 2010 by hgpu

The gputools package enables GPU computing in R

Joshua Buckner, Justin Wilson, Mark Seligman, Brian Athey, Stanley Watson, Fan Meng

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Tags: Computer science, CUBLAS, CUDA, nVidia, Package

October 28, 2010 by hgpu

Accelerating Large Graph Algorithms on the GPU Using CUDA

Pawan Harish, P. Narayanan

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce 8800 GTX

October 28, 2010 by hgpu

Adaptive Mesh Fluid Simulations on GPU

Peng Wang, Tom Abel, Ralf Kaehler

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, nVidia, nVidia GeForce 8800 GT

October 28, 2010 by hgpu

GPU-accelerated large-scale quantum molecular dynamics simulation of 3-dimensional C60 polymers

Toshiaki Iitaka

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Tags: Computational Physics, CUDA, Materials Science, Molecular dynamics, nVidia, nVidia GeForce 8800 GTX, Physics

October 28, 2010 by hgpu

GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics

Hsi-Yu Schive, Yu-Chih Tsai, Tzihong Chiueh

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia

October 28, 2010 by hgpu

High performance cellular level agent-based simulation with FLAME for the GPU

Paul Richmond, Dawn Walker, Simon Coakley, Daniela Romano

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Tags: Bioinformatics, Cellular modelling, Computational modelling, Computer science, CUDA, nVidia, Package

October 28, 2010 by hgpu

CrystalGPU: Transparent and Efficient Utilization of GPU Power

Abdullah Gharaibeh, Samer Al-Kiswany, Matei Ripeanu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Package, Performance

October 28, 2010 by hgpu

All-pairs shortest-paths for large graphs on the GPU

Gary J. Katz, Joseph T. Kider

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Tags: CUDA, Graph theory, Mathematics, nVidia, nVidia Quadro FX 5600

October 28, 2010 by hgpu

An integrated GPU power and performance model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, Performance

October 28, 2010 by hgpu

GPU as a General Purpose Computing Resource

Qihang Huang, Zhiyi Huang, Paul Werstein, Martin Purvis

Tags: Computer science, CUDA, nVidia, Performance, Programming techniques

October 27, 2010 by hgpu

Fast and scalable list ranking on the GPU

M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, List ranking, nVidia, nVidia GeForce GTX 280

October 27, 2010 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

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high performance computing on graphics processing units: hgpu.org

Adapting a message-driven parallel application to GPU-accelerated clusters

The gputools package enables GPU computing in R

Accelerating Large Graph Algorithms on the GPU Using CUDA

Adaptive Mesh Fluid Simulations on GPU

GPU-accelerated large-scale quantum molecular dynamics simulation of 3-dimensional C60 polymers

GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics

High performance cellular level agent-based simulation with FLAME for the GPU

CrystalGPU: Transparent and Efficient Utilization of GPU Power

All-pairs shortest-paths for large graphs on the GPU

An integrated GPU power and performance model

GPU as a General Purpose Computing Resource

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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