high performance computing on graphics processing units: hgpu.org

hgpu.org » Materials Science

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, Materials Science, Neural networks, Package, Python

November 6, 2022 by hgpu

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

Samuel S. Schoenholz, Ekin D. Cubuk

View

Download (PDF)

Source codes

Tags: Biochemistry, Chemistry, Computational Physics, CUDA, Fortran, Machine learning, Materials Science, Molecular dynamics, Neural networks, nVidia, Package, Physics, Python

December 15, 2019 by hgpu

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

K. Mills, I. Tamblyn

View

Download (PDF)

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

July 2, 2017 by hgpu

A hybrid algorithm for parallel molecular dynamics simulations

Chris M. Mangiardi, Ralf Meyer

View

Download (PDF)

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

November 3, 2016 by hgpu

Efficient molecular dynamics simulations with many-body potentials on graphics processing units

Zheyong Fan, Wei Chen, Ville Vierimaa, Ari Harju

View

Download (PDF)

Tags: Computational Physics, CUDA, Materials Science, Molecular dynamics, nVidia, Physics, Tesla K40

October 15, 2016 by hgpu

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

View

Download (PDF)

Source codes

Tags: Chemistry, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Materials Science, Molecular dynamics, MPI, nVidia, Package, performance portability, Tesla K20, Tesla K40

July 13, 2016 by hgpu

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Jeffrey Kelling, Geza Odor, Sibylle Gemming

View

Download (PDF)

Tags: Computational Physics, Computer science, CUDA, Materials Science, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce Titan Black, Statistical Mechanics, Tesla K80

June 7, 2016 by hgpu

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

Zhanghui Chen, Lin-Wang Wang

View

Download (PDF)

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

March 12, 2016 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

View

Download (PDF)

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

July 3, 2015 by hgpu

Research on double negative materials by using FDTD method based on GPUs

Chen Shen, Xian-liang Wu

View

Download (PDF)

Tags: CUDA, FDTD, Finite difference, Finite-difference time-domain, Materials Science, nVidia, nVidia GeForce GT 440, Physics

September 5, 2014 by hgpu

GPU/CPU Parallel Computation of Material Damage

Jie Shen, Diego Vela, Ankita Singh, Kexing Song, Guoshang Zhang, Bradon LaFreniere, Hao Chen

View

Download (PDF)

Tags: CAD, Computed tomography, CT, CUDA, Materials Science, nVidia, nVidia Quadro FX 1700, Tesla C1060, Tomography

June 19, 2014 by hgpu

Finite differences numerical method for two-dimensional superlattice Boltzmann transport equation and case comparison of CPU(C) and GPGPU(CUDA) implementations

Dmitri Priimak

View

Download (PDF)

Tags: Algorithms, Computational Physics, CUDA, Finite difference, Lattice Boltzmann model, Materials Science, nVidia, nVidia GeForce GTX 680, Physics

January 25, 2014 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

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

A hybrid algorithm for parallel molecular dynamics simulations

Efficient molecular dynamics simulations with many-body potentials on graphics processing units

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

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

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

Research on double negative materials by using FDTD method based on GPUs

GPU/CPU Parallel Computation of Material Damage

Finite differences numerical method for two-dimensional superlattice Boltzmann transport equation and case comparison of CPU(C) and GPGPU(CUDA) implementations

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)