high performance computing on graphics processing units: hgpu.org

hgpu.org » Computational Physics

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

Konstantin Herb, Pol Welter

View

Download (PDF)

Source codes

Tags: BLAS, Computational Physics, Computer science, CUDA, Numerical Analysis, nVidia, Package, Tesla V100

August 22, 2021 by hgpu

Lettuce: PyTorch-based Lattice Boltzmann Framework

Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank, Dirk Reith, Holger Foysi, Andreas Krämer

View

Download (PDF)

Source codes

Tags: Algorithms, Computational Physics, CUDA, Deep learning, Fluid dynamics, Lattice Boltzmann model, Machine learning, nVidia, nVidia GeForce RTX 2070, Package, Physics, Tesla V100

June 27, 2021 by hgpu

BASEMENT v3: a modular freeware for river process modelling over multiple computational backends

Davide Vanzo, Samuel Peter, Lukas Vonwiller, Matthias Buergler, Manuel Weberndorfer, Annunziato Siviglia, Daniel Conde, David F. Vetsch

View

Download (PDF)

Source codes

Tags: cfd, Computational Physics, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

February 28, 2021 by hgpu

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Thibaut Very, Isabelle Dupays, Zhi Wang, Théo Jaffrelot Inizan, Frédéric Célerse, Pengyu Ren, Jay W. Ponder, Jean-Philip Piquemal

View

Download (PDF)

Source codes

Tags: Biophysics, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia DGX-A100, OpenACC, Package, Physics, Tesla V100

November 8, 2020 by hgpu

Cross-platform programming model for many-core lattice Boltzmann simulations

Jonas Latt, Christophe Coreixas, Joël Beny

View

Download (PDF)

Tags: Algorithms, cfd, Computational Physics, CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 1080, nVidia GeForce RTX 2080, Physics

October 25, 2020 by hgpu

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

View

Download (PDF)

Tags: Computational Physics, CUDA, HPC, Machine learning, Mixed precision, Molecular dynamics, Molecular simulation, MPI, nVidia, OpenMP, Physics, Tesla V100

May 10, 2020 by hgpu

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

Denghui Lu, Han Wang, Mohan Chen, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang

View

Download (PDF)

Source codes

Tags: Computational Physics, CUDA, Heterogeneous systems, Molecular dynamics, Neural networks, nVidia, Package, Physics, Python, Tesla V100

May 3, 2020 by hgpu

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

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, James Ahrens

View

Download (PDF)

Source codes

Tags: Astrophysics, Compression, Computational Physics, Cosmology, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Physics, Tesla K80, Tesla P100, Tesla V100

April 5, 2020 by hgpu

Inline Vector Compression for Computational Physics

Will Trojak, Freddie Witherden

View

Download (PDF)

Source codes

Tags: Compression, Computational Physics, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, Package, Physics, Tesla V100

March 8, 2020 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

PySPH: a Python-based framework for smoothed particle hydrodynamics

Prabhu Ramachandran, Kunal Puri, Aditya Bhosale, A Dinesh, Abhinav Muta, Pawan Negi, Rahul Govind, Suraj Sanka, Pankaj Pandey, Chandrashekhar Kaushik, Anshuman Kumar, Ananyo Sen, Rohan Kaushik, Mrinalgouda Patil, Deep Tavker, Dileep Menon, Vikas Kurapati, Amal S Sebastian, Arkopal Dutt, Arpit Agarwal

View

Download (PDF)

Source codes

Tags: cfd, Computational Physics, CUDA, Fluid dynamics, Mathematical Software, nVidia, nVidia GeForce GTX 1050 Ti, OpenCL, Package

September 15, 2019 by hgpu

Efficient Interleaved Batch Matrix Solvers for CUDA

Andrew Gloster, Enda Carroll, Miguel Bustamante, Lennon O'Naraigh

View

Download (PDF)

Source codes

Tags: Algorithms, Computational Physics, Computer science, CUDA, Matrix inversion, nVidia, Package, Tesla V100

September 15, 2019 by hgpu

* * *

high performance computing on graphics processing units: hgpu.org

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

Lettuce: PyTorch-based Lattice Boltzmann Framework

BASEMENT v3: a modular freeware for river process modelling over multiple computational backends

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

Cross-platform programming model for many-core lattice Boltzmann simulations

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

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

Inline Vector Compression for Computational Physics

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

PySPH: a Python-based framework for smoothed particle hydrodynamics

Efficient Interleaved Batch Matrix Solvers for CUDA

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

Most viewed papers (last 30 days)