high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Chemistry

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Janus Juul Eriksen

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Tags: Chemical Physics, Chemistry, Mixed precision, nVidia, OpenACC, Physics, Tesla K40

September 27, 2016 by hgpu

Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

Bobo Shi

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Intel Xeon Phi, nVidia, OpenMP, Tesla K40, Thesis

September 5, 2016 by hgpu

An investigation of GPU-based stiff chemical kinetics integration methods

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Algorithms, Chemistry, Computational Physics, CUDA, nVidia, Physics, Tesla C2075

July 16, 2016 by hgpu

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

Ye Fang, Yun Ding, Wei P. Feinstein, David M. Koppelman, Juana Moreno, Mark Jarrell, J. Ramanujam, Michal Brylinski

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Tags: Algorithms, Chemistry, CUDA, Heterogeneous systems, Intel Xeon Phi, Medicine, Molecular dynamics, nVidia, nVidia GeForce GTX 980, OpenMP, Package

July 16, 2016 by hgpu

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

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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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

Optimization of Molecular Dynamics Simulation Code and Applications to Biomolecular Systems

David M. Bowman

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Tags: Biochemistry, Biomolecules, Chemistry, CUDA, Molecular dynamics, MPI, nVidia, OpenCL, OpenMP, Thesis

March 1, 2016 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

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Mark James Abraham, Teemu Murtola, Roland Schulz, Szilard Pall, Jeremy C. Smith, Berk Hess, Erik Lindahl

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Tags: Algorithms, Biomolecules, Chemistry, CUDA, Heterogeneous systems, Molecular simulation, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Tesla K40

July 20, 2015 by hgpu

Generating Efficient Tensor Contractions for GPUs

Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Boyana Norris

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Tags: Algorithms, Chemistry, Computer science, CUDA, Fluid dynamics, Machine learning, nVidia, OpenACC, Optimization

June 22, 2015 by hgpu

Quantum Chemistry for Solvated Molecules on Graphical Processing Units (GPUs) using Polarizable Continuum Models

Fang Liu, Nathan Luehr, Heather J. Kulik, Todd J. Martinez

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Tags: Biomolecules, Chemical Physics, Chemistry, CUDA, nVidia, nVidia GeForce GTX Titan, Physics

June 1, 2015 by hgpu

Nucleation Studies on Graphics Processing Units

Benjamin J. Block

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Tags: Anisotropy, ATI, ATI Radeon HD 6970, Biology, Chemistry, Differential equations, Ising model, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Physics, Tesla K20, Thesis

February 6, 2015 by hgpu

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Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Molecular Activity Prediction using Deep Learning Software Library

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

An investigation of GPU-based stiff chemical kinetics integration methods

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

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

Optimization of Molecular Dynamics Simulation Code and Applications to Biomolecular Systems

Scalable Metropolis Monte Carlo for simulation of hard shapes

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Generating Efficient Tensor Contractions for GPUs

Quantum Chemistry for Solvated Molecules on Graphical Processing Units (GPUs) using Polarizable Continuum Models

Nucleation Studies on Graphics Processing Units

Recent source codes

E2C-Sim: a simulator and workload generator for Edge-to-Cloud environments

Kernel Tuner

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

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