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Optimization of a discontinuous Galerkin solver with OpenCL and StarPU

Bérenger Bramas, Philippe Helluy, Laura Mendoza, Bruno Weber

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Tags: Algorithms, Mathematics, nVidia, nVidia GeForce GTX 1050, OpenCL, Package, PDEs, Tesla P100

February 2, 2020 by hgpu

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

C. Escalante, T. Morales de Luna, M.J. Castro

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Mathematics, Numerical Analysis, nVidia

June 17, 2017 by hgpu

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

J.E. Adsuara, I. Cordero-Carrion, P. Cerda-Duran, M.A. Aloy

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Tags: CUDA, Differential equations, Finite difference, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, Partial differential equations, PDEs, Tesla K40

May 2, 2017 by hgpu

Streaming GPU Singular Value and Dynamic Mode Decompositions

Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton

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Tags: Algorithms, Computer vision, CUDA, Mathematics, Matrix multiplication, Numerical Analysis, nVidia, Sparse matrix, Tesla K40

December 26, 2016 by hgpu

Feynman Machine: The Universal Dynamical Systems Computer

Eric Laukien, Richard Crowder, Fergal Byrne

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Tags: AMD Radeon R9 290, Artificial intelligence, ATI, Computer science, Deep learning, Mathematics, Neuroscience, OpenCL

September 20, 2016 by hgpu

An efficient numerical method for solving the Boltzmann equation in multidimensions

Giacomo Dimarco, Raphael Loubere, Jacek Narski, Thomas Rey

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Tags: Algorithms, Boltzmann equation, cfd, CUDA, Fluid dynamics, Mathematics, MPI, Numerical Analysis, nVidia, OpenMP, Physics

September 13, 2016 by hgpu

A New Architecture for Optimization Modeling Frameworks

Matt Wytock, Steven Diamond, Felix Heide, Stephen Boyd

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Tags: Algorithms, CUDA, Deep learning, Linear Algebra, Mathematics, nVidia, nVidia GeForce GTX Titan X, Package, Python

September 13, 2016 by hgpu

GPU-accelerated Gibbs Sampling

Alexander Terenin, Shawfeng Dong, David Draper

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Tags: Bayesian, big data, CUDA, nVidia, Statistics, Tesla K20

August 18, 2016 by hgpu

Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

Kevin L. Keys, Gary K. Chen, Kenneth Lange

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Tags: Bioinformatics, Genetics, Machine learning, nVidia, OpenCL, Package, Statistics, Tesla C2050

August 8, 2016 by hgpu

Fast hyperbolic Radon transform represented as convolutions in log-polar coordinates

Viktor V. Nikitin, Fredrik Andersson, Marcus Carlsson, Anton A. Duchkov

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Tags: CUDA, FFT, Geoscience, Mathematics, Numerical Analysis, nVidia, Radon transform, Seismic modeling

May 3, 2016 by hgpu

GPU-accelerated stochastic predictive control of drinking water networks

Ajay K. Sampathirao, Pantelis Sopasakis, Alberto Bemporad, Panagiotis Patrinos

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Tags: CUDA, Mathematics, nVidia, Tesla C2075

April 6, 2016 by hgpu

GPU-accelerated Bernstein-Bezier discontinuous Galerkin methods for wave problems

Jesse Chan, T. Warburton

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Tags: Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX 980, OCCA

December 22, 2015 by hgpu

Deep Learning for Compilers

Deep Learning for Compilers

PyMatting: A Python Library for Alpha Matting

PyMatting: A Python Library for Alpha Matting

vilib: CUDA Visual Library by RPG

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Error-bounded Lossy Data Compressor (for floating-point/integer datasets)

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Learnergy: Energy-based Machine Learners

Towards automated kernel selection in machine learning systems: A SYCL case study

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

Automated test generation for OpenCL kernels using fuzzing and constraint solving

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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