high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

Automated Dynamic Analysis of CUDA Programs

Michael Boyer, Kevin Skadron, Westley Weimer

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

February 12, 2011 by hgpu

EigenCFA: accelerating flow analysis with GPUs

Tarun Prabhu, Shreyas Ramalingam, Matthew Might, Mary Hall

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Tags: Computer science, CUBLAS, Flow analysis, nVidia, nVidia GeForce GTX 480, Optimization

February 11, 2011 by hgpu

Partitioning streaming parallelism for multi-cores: a machine learning based approach

Zheng Wang, Michael F. P. O'Boyle

Tags: Computer science, Optimization, Programming techniques

February 8, 2011 by hgpu

Optimal automatic multi-pass shader partitioning by dynamic programming

Alan Heirich

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Tags: Algorithms, Compilers, Computer science, Image generation, OpenGL, Optimization, Shaders

February 7, 2011 by hgpu

Fast Schedulability Analysis Using Commodity Graphics Hardware

Jimin Feng, Samarjit Chakraborty, Bertil Schmidt, Weiguo Liu, Unmesh D. Bordoloi

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Tags: Cg, Computer science, nVidia, nVidia GeForce 8800 GTX, OpenGL, Optimization

January 25, 2011 by hgpu

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Xiao-Long Wu, Nady Obeid, Wen-Mei Hwu

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

January 24, 2011 by hgpu

Program Optimization Strategies for Data-Parallel Many-Core Processors

Shane Ryoo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Thesis

January 24, 2011 by hgpu

A Multi-Stage CUDA Kernel for Floyd-Warshall

Ben Lund, Justin W Smith

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Tags: Computer science, CUDA, nVidia, Optimization, Performance, Programming techniques, Tesla C1060

January 18, 2011 by hgpu

A memory optimization technique for software-managed scratchpad memory in GPUs

Maryam Moazeni, Alex A. T. Bui, Majid Sarrafzadeh

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Tags: Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 5600, Optimization

January 11, 2011 by hgpu

Optimization of tele-immersion codes

Albert Sidelnik, I-Jui Sung, Wanmin Wu, Maria Jesus Garzaran, Wen-mei Hwu, Klara Nahrstedt, David Padua, and Sanjay J. Patel

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 280, Optimization

January 11, 2011 by hgpu

Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

Aparna Chandramowlishwarany, Kamesh Madduri, Richard Vuduc

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Tags: Computer science, Fast multipole method, Optimization, Performance

January 11, 2011 by hgpu

ClearPath: highly parallel collision avoidance for multi-agent simulation

Stephen J. Guy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Ming Lin, Dinesh Manocha, Pradeep Dubey

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Tags: Artificial intelligence, Computer science, Larrabee, Optimization

January 8, 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

Automated Dynamic Analysis of CUDA Programs

EigenCFA: accelerating flow analysis with GPUs

Partitioning streaming parallelism for multi-cores: a machine learning based approach

Optimal automatic multi-pass shader partitioning by dynamic programming

Fast Schedulability Analysis Using Commodity Graphics Hardware

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Program Optimization Strategies for Data-Parallel Many-Core Processors

A Multi-Stage CUDA Kernel for Floyd-Warshall

A memory optimization technique for software-managed scratchpad memory in GPUs

Optimization of tele-immersion codes

Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

ClearPath: highly parallel collision avoidance for multi-agent simulation

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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