high performance computing on graphics processing units: hgpu.org

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Portable GPU-Based Artificial Neural Networks for Accelerated Data-Driven Modeling

Zheng Yi Wu, Mahmoud Elmaghraby

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 460, nVidia Quadro FX 770 M, OpenCL

March 18, 2015 by hgpu

Lessons learned from contrasting a BLAS kernel implementations

Andres More

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Tags: BLAS, Computer science, CUDA, nVidia, nVidia Quadro FX 770 M, Performance

December 12, 2013 by hgpu

Full Covariance Gaussian Mixture Models Evaluation on GPU

Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka

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Tags: Algorithms, ATI, ATI Mobility Radeon HD 5470, ATI Radeon HD 5670, ATI Radeon HD 5870, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 580, nVidia Quadro FX 770 M, OpenCL, Signal processing, Speech recognition

March 2, 2013 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

Efficient planar features matching for robot localization using GPU

Baptiste Charmette, Eric Royer, Frederic Chausse

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Tags: Computer science, Computer vision, CUDA, Image recognition, nVidia, nVidia Quadro FX 770 M, SIFT

March 20, 2011 by hgpu

Enhanced molecular dynamics performance with a programmable graphics processor

D. C. Rapaport

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Tags: Chemistry, Computational chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia Quadro FX 770 M, Physics

November 13, 2010 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

Portable GPU-Based Artificial Neural Networks for Accelerated Data-Driven Modeling

Lessons learned from contrasting a BLAS kernel implementations

Full Covariance Gaussian Mixture Models Evaluation on GPU

Acceleration of TM cylinder EFIE with CUDA

Efficient planar features matching for robot localization using GPU

Enhanced molecular dynamics performance with a programmable graphics processor

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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