high performance computing on graphics processing units: hgpu.org

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Efficient Parallel Proximity Queries and an Application to Highly Complex Motion Planning Problems with Many Narrow Passages

Rainer Erbes

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5000, Thesis

June 8, 2013 by hgpu

High-Order Schemes for the Shallow Water Equations on GPUs

Espen Graff Berglie

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Tags: Computer science, CUDA, Mathematics, nVidia, nVidia Quadro FX 5000, Thesis

May 4, 2013 by hgpu

Accelerating Cost Aggregation for Real-Time Stereo Matching

Jianbin Fang, Ana Lucia Varbanescu, Jie Shen, Henk Sips, Gorkem Saygili, Laurens van der Maaten

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Tags: Algorithms, Image processing, nVidia, nVidia Quadro FX 5000, nVidia Quadro NVS 140 M, OpenCL, Optimization

October 13, 2012 by hgpu

Large Scale Finite Element Analysis Using GPU Parallel Computing

Ashkan Akbariyeh

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Tags: Algorithms, Computer science, CUDA, Finite element method, nVidia, nVidia Quadro FX 5000, Sparse matrix, Thesis

August 8, 2012 by hgpu

Using GPU-based Computing To Accelerate Finite Element Problems

Jacob Watt

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Tags: Computer science, CUDA, Finite element method, Linear Algebra, nVidia, nVidia Quadro FX 5000, Thesis, Visualization

August 8, 2012 by hgpu

GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Chao Peng, Yong Cao

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Tags: Algorithms, Computer science, CUDA, Mesh simplification, nVidia, nVidia Quadro FX 5000, Rendering

January 3, 2012 by hgpu

Randomized selection on the GPU

Laura Monroe, Joanne Wendelberger, Sarah Michalak

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, nVidia Quadro FX 5000, nVidia Quadro FX 6000, Optimization, Presentation, Probability, Programming techniques

September 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

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high performance computing on graphics processing units: hgpu.org

Efficient Parallel Proximity Queries and an Application to Highly Complex Motion Planning Problems with Many Narrow Passages

High-Order Schemes for the Shallow Water Equations on GPUs

Accelerating Cost Aggregation for Real-Time Stereo Matching

Large Scale Finite Element Analysis Using GPU Parallel Computing

Using GPU-based Computing To Accelerate Finite Element Problems

GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Randomized selection on the GPU

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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