high performance computing on graphics processing units: hgpu.org

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Architecting graphics processors for non-graphics compute acceleration

Tor M. Aamodt

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Tags: Computer science, Hardware Architecture, nVidia, Review

February 20, 2011 by hgpu

Design Space Exploration for GPU-Based Architecture

Zhenyu Ye

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Tags: Computer science, CUDA, Factorization, Hardware Architecture, Memory model, nVidia, nVidia GeForce 8800 GTX, Review, Thesis

February 20, 2011 by hgpu

Fast Exact String Matching on the GPU

Michael C. Schatz, Cole Trapnell

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Tags: Algorithms, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Search, String matching, Suffix trees

February 20, 2011 by hgpu

Program Optimization Study on a 128-Core GPU

Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain-Zee Ueng, and Wen-mei W. Hwu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Programming techniques

February 20, 2011 by hgpu

How GPUs Can Improve the Quality of Magnetic Resonance Imaging

Sam S. Stone, Haoran Yi, Justin P. Haldar, Wen-mei W. Hwu, Bradley P. Sutton, and Zhi-Pei Liang

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Tags: CUDA, FFT, Image processing, Image reconstruction, Magnetic resonance imaging, Medicine, MRI, nVidia, nVidia GeForce 8800 GTX

February 20, 2011 by hgpu

MCUDA: An Efficient Implementation of CUDA Kernels on Multi-cores

John A. Stratton, Sam S. Stone, and Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, Package

February 20, 2011 by hgpu

Efficient compilation of fine-grained SPMD-threaded programs for multicore CPUs

John A. Stratton, Vinod Grover, Jaydeep Marathe, Bastiaan Aarts, Mike Murphy, Ziang Hu, Wen-mei W. Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Programming techniques

February 20, 2011 by hgpu

XMalloc: A Scalable Lock-free Dynamic Memory Allocator for Many-core Machines

Xiaohuang Huang, Christopher I. Rodrigues, Stephen Jones, Ian Buck, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, Memory, Memory model, nVidia, Tesla C1060

February 20, 2011 by hgpu

Data Layout Transformation Exploiting Memory-Level Parallelism in Structured Grid Many-Core Applications

I-Jui Sung, John A. Stratton, Wen-Mei W. Hwu

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Tags: Compilers, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 280

February 20, 2011 by hgpu

Accelerating Particle Image Velocimetry Using Hybrid Architectures

Vivek Venugopal, Cameron D. Patterson, Kevin Shinpaugh

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Tags: Computer science, CUDA, FPGA, nVidia, Tesla C1060, Visualization

February 19, 2011 by hgpu

Programmability: Design Costs and Payoffs using AMD GPU Streaming Languages and Traditional Multi-Core Libraries

Rick Weber, Robert J. Harrison, Gregory D. Peterson

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Tags: ATI CAL, ATI Radeon HD 4870, ATI Stream, Brook, Computer science, Review

February 19, 2011 by hgpu

Decoupled Access/Execute Metaprogramming for GPU-Accelerated Systems

Lee Howes, Anton Lokhmotov, Paul H.J. Kelly, Alastair F. Donaldson

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Tags: CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX 280, OpenCL

February 19, 2011 by hgpu

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

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high performance computing on graphics processing units: hgpu.org

Applications

Architecting graphics processors for non-graphics compute acceleration

Design Space Exploration for GPU-Based Architecture

Fast Exact String Matching on the GPU

Program Optimization Study on a 128-Core GPU

How GPUs Can Improve the Quality of Magnetic Resonance Imaging

MCUDA: An Efficient Implementation of CUDA Kernels on Multi-cores

Efficient compilation of fine-grained SPMD-threaded programs for multicore CPUs

XMalloc: A Scalable Lock-free Dynamic Memory Allocator for Many-core Machines

Data Layout Transformation Exploiting Memory-Level Parallelism in Structured Grid Many-Core Applications

Accelerating Particle Image Velocimetry Using Hybrid Architectures

Programmability: Design Costs and Payoffs using AMD GPU Streaming Languages and Traditional Multi-Core Libraries

Decoupled Access/Execute Metaprogramming for GPU-Accelerated Systems

Recent source codes

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

HDM: Home made Diffusion Models

General Matrix Multiplication (GEMM)

CrossTL: Universal Programming Language & Translator

TBD-GPU

DG-SWEM - The Discontinuous Galerkin Shallow Water Equation Model

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

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