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Adaptable particle-in-cell algorithms for graphical processing units

Viktor K. Decyk, Tajendra V. Singh

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Tags: CUDA, nVidia, Particle-in-cell methods, Physics, Presentation, Tesla C1060

January 26, 2011 by hgpu

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

S. A. Arul Shalom, Manoranjan Dash, Minh Tue, Nithin Wilson

Tags: Computer science, CUDA, GPU cluster, nVidia

January 26, 2011 by hgpu

Supercomputing with toys: harnessing the power of NVIDIA 8800GTX and playstation 3 for bioinformatics problem

Justin Wilson, Manhong Dai, Elvis Jakupovic, Stanley Watson, Fan Meng

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Tags: Bioinformatics, Cell processor, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance, Playstation, RapidMind

January 26, 2011 by hgpu

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

Jarad Niemi, Matthew Wheeler

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Tags: Bayesian, Biology, Chemistry, CUDA, nVidia, Stochastic simulation, Tesla T10

January 26, 2011 by hgpu

CUDASA: Compute Unified Device and Systems Architecture

M. Strengert, C. Muller, C. Dachsbacher, and T. Ertl

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Tags: Computer science, CUDA, GPU cluster, High-level Languages, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600

January 24, 2011 by hgpu

Sparse regularization in MRI iterative reconstruction using GPUs

Yue Zhuo, Bradley Sutton, Xiao-Long Wu, Justin Haldar, Wen-mei Hwu, Zhipei Liang

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Tags: CUDA, Image processing, Image reconstruction, MRI, nVidia, nVidia GeForce GTX 280, Sparse matrix

January 24, 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

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Y. Zhuo, X-L. Wu, J. P. Haldar, W-M. W. Hwu, Z-P. Liang, and B. P. Sutton

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Tags: CUDA, Image processing, Image reconstruction, MRI, nVidia, Tesla S1070

January 24, 2011 by hgpu

Accelerating iterative field-compensated MR image reconstruction on GPUs

Yue Zhuo, Xiao-Long Wu, Justin P. Haldar, Wen-mei Hwu, Zhi-pei Liang, Bradley P. Sutton

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Tags: Conjugate gradient solver, CUDA, Image processing, Image reconstruction, Medicine, MRI, nVidia, nVidia GeForce GTX 280

January 24, 2011 by hgpu

Data Layout Transformation for Structured-Grid Codes on GPU

I-Jui Sung, Wen-Mei Hwu

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280

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

Efficient Parallel Scan Algorithms for GPUs

Shubhabrata Sengupta, Mark Harris, Michael Garland

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Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Package

January 24, 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

Adaptable particle-in-cell algorithms for graphical processing units

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

Supercomputing with toys: harnessing the power of NVIDIA 8800GTX and playstation 3 for bioinformatics problem

Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units

CUDASA: Compute Unified Device and Systems Architecture

Sparse regularization in MRI iterative reconstruction using GPUs

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Accelerating iterative field-compensated MR image reconstruction on GPUs

Data Layout Transformation for Structured-Grid Codes on GPU

Program Optimization Strategies for Data-Parallel Many-Core Processors

Efficient Parallel Scan Algorithms for GPUs

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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