Computational chemistry

hgpu.org » Computational chemistry

Code Optimization on GPUs

Changwan Hong

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Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

Deep Learning for Computational Chemistry

Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu

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Tags: Algorithms, Chemistry, Computational chemistry, Computer science, Deep learning, Machine learning, Neural networks, Review

January 19, 2017 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

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

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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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

Modernizing the core quantum chemistry algorithms

Andrey Asadchev

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, nVidia, Programming techniques, Tesla C2050, Thesis

June 8, 2013 by hgpu

Implementing Continuous Integration Software in an Established Computational Chemistry Software Package

Robin M. Betz, Ross C. Walker

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Tags: Benchmarking, Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, Software Engineering, Tesla M2090

May 21, 2013 by hgpu

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Vlad Slavici

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, MapReduce, Matrix multiplication, nVidia, Tesla M2090, Thesis

April 3, 2013 by hgpu

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Vlad Slavici, Raghu Varier, Gene Cooperman, Robert J. Harrison

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Tags: Chemistry, Computational chemistry, Computer science, CUBLAS, CUDA, GPU cluster, nVidia, Tesla M2090

November 6, 2012 by hgpu

Solving Molecular Distance Geometry Problems in OpenCL

Istvan Lorentz, Razvan Andonie, Levente Fabry-Asztalos

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Tags: Chemistry, Computational chemistry, Molecular modeling, nVidia, nVidia GeForce GTX 560 Ti, OpenCL

June 27, 2012 by hgpu

Acceleration of computational quantum chemistry by heterogeneous computer architectures

Yuki Furukawa, Ryota Koga, Koji Yasuda

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Heterogeneous systems, Molecular modeling, nVidia, nVidia GeForce GTX 580

November 30, 2011 by hgpu

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

Long Wang, Weile Jia, Xuebin Chi, Yue Wu, Weiguo Gao, Lin-Wang Wang

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Tags: Algorithms, Computational chemistry, CUDA, GPU cluster, MPI, nVidia, Performance, Physics, Tesla C2050

November 14, 2011 by hgpu

Many-body quantum chemistry on graphics processing units

A. Eugene DePrince III, Jeff R. Hammond, Stephen K. Gray

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Heterogeneous systems, Matrix multiplication, Molecular modeling, nVidia, Tesla C2050

November 11, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Code Optimization on GPUs

Deep Learning for Computational Chemistry

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

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

Modernizing the core quantum chemistry algorithms

Implementing Continuous Integration Software in an Established Computational Chemistry Software Package

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Solving Molecular Distance Geometry Problems in OpenCL

Acceleration of computational quantum chemistry by heterogeneous computer architectures

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

Many-body quantum chemistry on graphics processing units

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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