high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications

Graphics Processing Units and High-Dimensional Optimization

Hua Zhou, Kenneth Lange, Marc A. Suchard

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Tags: Block relaxation, CUBLAS, CUDA, EM and MM algorithms, Mathematics, Multidimensional scaling, Nonnegative matrix factorization, nVidia, nVidia GeForce GTX 280, PET scanning, Statistics

October 30, 2010 by hgpu

How GPUs Work

David Luebke, Greg Humphreys

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Tags: Computer science, Review

October 30, 2010 by hgpu

GPUs: A Closer Look

Kayvon Fatahalian, Mike Houston

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Tags: Computer science, Review

October 30, 2010 by hgpu

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Ivan S. Ufimtsev, Todd J. Martinez

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

Mapping computational concepts to GPUs

Mark Harris

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Tags: Computer science, Review

October 30, 2010 by hgpu

Accelerating Density Functional Calculations with Graphics Processing Unit

Koji Yasuda

Tags: Chemistry, Computational chemistry, Molecular modeling

October 30, 2010 by hgpu

A Memory Model for Scientific Algorithms on Graphics Processors

Naga K. Govindaraju, Scott Larsen, Jim Gray, Dinesh Manocha

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Tags: Algorithms, Computer science, FFT, Memory model, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, nVidia GeForce 7900 GTX, Sorting

October 30, 2010 by hgpu

A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

Jeremy W. Sheaffer, David P. Luebke, Kevin Skadron

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Tags: Computer science, Error recovery

October 30, 2010 by hgpu

Performance enhancement of MAGIC FDTD-PIC plasma-wave simulations using GPU processing

Andrew J. Woods, Lars D. Ludeking

Tags: Physics, Plasma physics

October 30, 2010 by hgpu

Optimizing data intensive GPGPU computations for DNA sequence alignment

Cole Trapnell, Michael C. Schatz

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment, Short read mapping, Suffix trees

October 30, 2010 by hgpu

Accelerating molecular modeling applications with graphics processors

John E. Stone, James C. Phillips, Peter L. Freddolino, David J. Hardy, Leonardo G. Trabuco, Klaus Schulten

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Tags: Chemistry, Computational chemistry, CUDA, Electrostatic potential, Molecular dynamics, Molecular modeling, Multilevel summation, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

Mars: a MapReduce framework on graphics processors

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang

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Tags: Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce 8800 GTX, Package, Programming techniques, Web Analysis

October 30, 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

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high performance computing on graphics processing units: hgpu.org

Applications

Graphics Processing Units and High-Dimensional Optimization

How GPUs Work

GPUs: A Closer Look

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Mapping computational concepts to GPUs

Accelerating Density Functional Calculations with Graphics Processing Unit

A Memory Model for Scientific Algorithms on Graphics Processors

A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

Performance enhancement of MAGIC FDTD-PIC plasma-wave simulations using GPU processing

Optimizing data intensive GPGPU computations for DNA sequence alignment

Accelerating molecular modeling applications with graphics processors

Mars: a MapReduce framework on graphics processors

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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