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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

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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

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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

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

March 8, 2020 by hgpu

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Leonardo Solis Vasquez

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, FPGA, Heterogeneous systems, Macromolecule, nVidia, OpenCL, Package, Physics, Tesla V100, Thesis

December 29, 2019 by hgpu

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

Samuel S. Schoenholz, Ekin D. Cubuk

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

December 15, 2019 by hgpu

waLBerla: A block-structured high-performance framework for multiphysics simulations

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

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Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

PANNA: Properties from Artificial Neural Network Architectures

Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli

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Tags: Deep learning, Machine learning, Molecular dynamics, Neural networks, Package, Physics, TensorFlow

July 10, 2019 by hgpu

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

Axel Hübl

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Tags: CUDA, HPC, nVidia, Package, Particle-in-cell methods, Physics, Tesla P100, Thesis

July 4, 2019 by hgpu

Classify QCD phase transition with deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stoecker, Xin-Nian Wang

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Tags: Deep learning, HEP, Machine learning, Neural networks, OpenCL, Package, Pattern recognition, Phase transition, Physics, QCD

June 2, 2019 by hgpu

TensorNetwork: A Library for Physics and Machine Learning

Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, Stefan Leichenauer

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Tags: Machine learning, Package, Physics, TensorFlow

May 8, 2019 by hgpu

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

Harri Renney, Benedict R. Gaster, Tom Mitchell

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Tags: AMD Radeon 530, ATI, FDTD, Finite-difference time-domain, OpenCL, OpenGL, Package, Physics

April 14, 2019 by hgpu

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Kun Yang, Yi-Fan Chen, Georgios Roumpos, Chris Colby, John Anderson

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Tags: Cloud, CUDA, Ising model, Machine learning, Monte Carlo simulation, Neural networks, nVidia, Package, Physics, Tesla V100, TPU

April 7, 2019 by hgpu

high performance computing on graphics processing units: hgpu.org

Packages

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

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

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

waLBerla: A block-structured high-performance framework for multiphysics simulations

PANNA: Properties from Artificial Neural Network Architectures

PIConGPU: Predictive Simulations of Laser-Particle Accelerators with Manycore Hardware

Classify QCD phase transition with deep learning

TensorNetwork: A Library for Physics and Machine Learning

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

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