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Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

K. Mills, I. Tamblyn

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Tags: Condensed matter, CUDA, Deep learning, Ising model, Materials Science, Neural networks, nVidia, Phase transition, Physics, Tesla K40

July 2, 2017 by hgpu

Brownian Dynamics of Active Sphere Suspensions Confined Near a No-Slip Boundary

Florencio Balboa Usabiaga, Blaise Delmotte, Aleksandar Donev

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Tags: Condensed matter, CUDA, nVidia, Package, Physics, pyCUDA, Soft Condensed Matter

December 6, 2016 by hgpu

A hybrid algorithm for parallel molecular dynamics simulations

Chris M. Mangiardi, Ralf Meyer

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Tags: Condensed matter, Intel Xeon Phi, Materials Science, Molecular dynamics

November 3, 2016 by hgpu

OpenMP, OpenMP/MPI, and CUDA/MPI C programs for solving the time-dependent dipolar Gross-Pitaevskii equation

Vladimir Loncar, Luis E. Young-S., Srdjan Skrbic, Paulsamy Muruganandam, Sadhan K. Adhikari, Antun Balaz

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Tags: Condensed matter, CUDA, MPI, nVidia, OpenMP, Package, Physics, Tesla M2090

October 22, 2016 by hgpu

GPU Based Detection of Topological Changes in Voronoi Diagrams

Massimo Bernaschi, Matteo Lulli, Mauro Sbragaglia

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Tags: Computational Physics, Condensed matter, CUDA, Lattice Boltzmann model, nVidia, Physics, Soft Condensed Matter, Voronoi diagram

July 8, 2016 by hgpu

SGO: An ultrafast engine for atomic structure global optimization by differential evolution

Zhanghui Chen, Lin-Wang Wang

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Tags: Condensed matter, Differential evolution, Materials Science, nVidia, nVidia GeForce GTX 980

March 12, 2016 by hgpu

Aging in the three-dimensional Random Field Ising Model

Sebastian von Ohr, Markus Manssen, Alexander K. Hartmann

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Tags: Condensed matter, Intel Xeon Phi, Statistical Mechanics

January 14, 2016 by hgpu

Exact diagonalization of quantum lattice models on coprocessors

Topi Siro, Ari Harju

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Tags: Condensed matter, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, OpenMP, Physics, Tesla K40

November 4, 2015 by hgpu

Scalable Metropolis Monte Carlo for simulation of hard shapes

Joshua A. Anderson, M. Eric Irrgang, Sharon C. Glotzer

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Tags: Benchmarking, Chemistry, Condensed matter, CUDA, Monte Carlo simulation, nVidia, Physics, Soft Condensed Matter, Tesla K20

September 17, 2015 by hgpu

A parallel algorithm for implicit depletant simulations

Jens Glaser, Andrew S. Karas, Sharon C. Glotzer

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Tags: Condensed matter, CUDA, Molecular dynamics, nVidia, Package, Physics, Soft Condensed Matter, Tesla K20, Tesla K80

August 31, 2015 by hgpu

GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Ursula Iturraran-Viveros, Miguel Molero-Armenta

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Tags: Condensed matter, Instrumentation and measurement, nVidia, OpenCL, Package, Physics, PyOpenCL, Python

August 21, 2015 by hgpu

GPU phase-field lattice Boltzmann simulations of growth and motion of a binary alloy dendrite

T. Takaki, R. Rojas, M Ohno, T. Shimokawabe, T. Aoki

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Tags: Alloys, Condensed matter, CUDA, Lattice Boltzmann model, Materials Science, nVidia, Physics, Tesla K20

July 3, 2015 by hgpu

Neural Query Language: A Knowledge Base Query Language for Tensorflow

Neural Query Language: A Knowledge Base Query Language for Tensorflow

NMODL: Code Generation Framework For NEURON MODeling Language

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

cuda-benches: collection of small CUDA benchmarking programs

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Simulee: Charactering and Detecting CUDA Program Bugs

Charactering and Detecting CUDA Program Bugs

TensorNetwork: A TensorFlow library for easy and efficient manipulation of tensor networks

TensorNetwork: A Library for Physics and Machine Learning

jive

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

caffe-mc-opencl: Caffe Deep Learning Software with Model Compression by Sparse Coding (Backend: OpenCL)

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Full-stack Optimization for Accelerating CNNs with FPGA Validation

Full-stack Optimization for Accelerating CNNs with FPGA Validation

AdaNet: Fast and flexible AutoML with learning guarantees

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

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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

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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