high performance computing on graphics processing units: hgpu.org

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Using Shared Memory as a Cache in Cellular Automata Water Flow Simulations on GPUs

Pawel Topa, Pawel Mlocek

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Tags: Algorithms, Cellular automata, CUDA, Fluid dynamics, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 295, nVidia Quadro FX 4800, nVidia Quadro NVS 140 M

December 6, 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

Hyper neural network on OpenCL

Frantisek Sindelar

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Tags: Algorithms, Compression, Computer science, Genetic programming, Neural networks, nVidia, nVidia Quadro NVS 140 M, OpenCL, Thesis

January 6, 2012 by hgpu

Acceleration of TM cylinder EFIE with CUDA

Tyler Killian, Daniel L. Faircloth, Sadasiva M. Rao

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Tags: Conjugate gradient solver, CUDA, Electrodynamics, Field equations, nVidia, nVidia GeForce 8800 GT, nVidia Quadro FX 770 M, nVidia Quadro NVS 140 M, Physics

September 1, 2011 by hgpu

A self-organization based optical flow estimator with GPU implementation (thesis)

Manish Shiralkar

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 2011 by hgpu

A self-organization based optical flow estimator with GPU implementation

Manish Shiralkar, Robert Schalkoff

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 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

Using Shared Memory as a Cache in Cellular Automata Water Flow Simulations on GPUs

Accelerating Cost Aggregation for Real-Time Stereo Matching

Hyper neural network on OpenCL

Acceleration of TM cylinder EFIE with CUDA

A self-organization based optical flow estimator with GPU implementation (thesis)

A self-organization based optical flow estimator with GPU implementation

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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