high performance computing on graphics processing units: hgpu.org

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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

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

Compiler Support for High-level GPU Programming

Tianyi David Han and Tarek S. Abdelrahman

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Package

February 19, 2011 by hgpu

A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs

W. Honig, F. Schmitt, R. Widera, H. Burau, G. Juckeland, M. S. Muller, and M. Bussmann

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Tags: ATI Radeon HD 5770, Computer science, CUDA, nVidia, OpenCL, Particle-in-cell methods, Plasma physics, Tesla C2050, Tesla S1070

February 19, 2011 by hgpu

GPU Accelerated Scalable Parallel Random Number Generators

Shuang Gao, Gregory D. Peterson

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 480, Pseudo-random number generators, Tesla S1060

February 19, 2011 by hgpu

Faster File Matching using GPGPUs

Deephan Mohan and John Cavazos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Security, String matching

February 19, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Decoupled Access/Execute Metaprogramming for GPU-Accelerated Systems

Compiler Support for High-level GPU Programming

A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs

GPU Accelerated Scalable Parallel Random Number Generators

Faster File Matching using GPGPUs

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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