high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

A Case Study of SWIM: Optimization of Memory Intensive Application on GPGPU

Wei Yi, Yuhua Tang, Guibin Wang, Xudong Fang

Tags: Computer science, Memory, nVidia, nVidia GeForce GTX 295, Optimization, Performance

April 4, 2011 by hgpu

Exploiting SIMD extensions for linear image processing with OpenCL

S. Antao, L. Sousa

Tags: Image processing, OpenCL, Optimization

March 31, 2011 by hgpu

A parallelization cost model for GPU

Zhang Dan, Zhao Rongcai, Han Lin, Wang Tao, Qu Jin

Tags: Computer science, Optimization, Performance

March 27, 2011 by hgpu

Optimization and Implementation of LBM Benchmark on Multithreaded GPU

Xiaoguang Ren, Yuhua Tang, Guibin Wang, Tao Tang, Xudong Fang

Tags: ATI, Brook, Fluid dynamics, Lattice Boltzmann model, Optimization

March 25, 2011 by hgpu

Joint Forces: From Multithreaded Programming to GPU Computing

Frank Feinbube, Peter Troger, Andreas Polze

Tags: Computer science, Optimization, Performance

March 20, 2011 by hgpu

Implementing a GPU Programming Model on a non-GPU Accelerator Architecture

Stephen M. Kofsky, Daniel R. Johnson, John A. Stratton, Wen-Mei W. Hwu, Sanjay J. Patel, Steven S. Lumetta

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Tags: Computer science, CUDA, nVidia, Optimization, Programming techniques, Tesla T10

March 19, 2011 by hgpu

High-Throughput Transaction Executions on Graphics Processors

Bingsheng He, Jeffrey Xu Yu

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Tags: Computer science, CUDA, Databases, nVidia, Optimization, Tesla C1060

March 17, 2011 by hgpu

Patterns of Inefficient Performance Behavior in GPU Applications

Dominic Eschweiler, Daniel Becker, Felix Wolf

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla T10

March 16, 2011 by hgpu

Comparing GPU and CPU in OLAP Cubes Creation

Krzysztof Kaczmarski

Tags: Computer science, CUDA, Databases, nVidia, Optimization

March 13, 2011 by hgpu

A Quantitative Performance Analysis Model for GPU Architectures

Yao Zhang, John D. Owens

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Tags: Computer science, CUDA, Hardware Architecture, Linear Algebra, nVidia, nVidia GeForce GTX 285, Optimization

March 13, 2011 by hgpu

Gyrokinetic Particle-in-Cell Optimization on Emerging Multi- and Manycore Platforms

Kamesh Madduri, Eun-Jin Im, Khaled Z. Ibrahim, Samuel Williams, Stephane Ethier, Leonid Oliker

Tags: CUDA, nVidia, Optimization, Particle-in-cell methods, Plasma physics, Tesla C2050

March 9, 2011 by hgpu

Speculative Execution on Multi-GPU Systems

Gregory Diamos, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 9800 GX2, Optimization, Performance, Tesla S870

March 6, 2011 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

A Case Study of SWIM: Optimization of Memory Intensive Application on GPGPU

Exploiting SIMD extensions for linear image processing with OpenCL

A parallelization cost model for GPU

Optimization and Implementation of LBM Benchmark on Multithreaded GPU

Joint Forces: From Multithreaded Programming to GPU Computing

Implementing a GPU Programming Model on a non-GPU Accelerator Architecture

High-Throughput Transaction Executions on Graphics Processors

Patterns of Inefficient Performance Behavior in GPU Applications

Comparing GPU and CPU in OLAP Cubes Creation

A Quantitative Performance Analysis Model for GPU Architectures

Gyrokinetic Particle-in-Cell Optimization on Emerging Multi- and Manycore Platforms

Speculative Execution on Multi-GPU Systems

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

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