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Quantum Monte Carlo on graphical processing units

COMPUTER PHYSICS COMMUNICATIONS 2007年第3期177卷 298-306页

作者： Anderson, Amos G. Goddard, William A., III Schroeder, Peter CALTECH Mat & Proc Simulat Ctr Div Chem & Chem Engn MC 139-74 Pasadena CA 91125 USA CALTECH Dept Comp Sci Pasadena CA 91125 USA

Quantum Monte Carlo (QMC) is among the most accurate methods for solving the time independent Schrodinger equation. Unfortunately, the method is very expensive and requires a vast array of computing resources in order to obtain results of a reasonable convergence level. On the other hand, the method is not only easily parallelizable across CPU clusters, but as we report here, it also has a high degree of data parallelism. This facilitates the use of recent technological advances in graphical processing units (GPUs), a powerful type of processor well known to computer gamers. In this paper we report on an end-to-end QMC application with core elements of the algorithm running on a GPU. With individual kernels achieving as much as 30x speed up, the overall application performs at up to 6x faster relative to an optimized CPU implementation, yet requires only a modest increase in hardware cost. This demonstrates the speedup improvements possible for QMC in running on advanced hardware, thus exploring a path toward providing QMC level accuracy as a more standard tool. The major current challenge in running codes of this type on the GPU arises from the lack of fully compliant IEEE floating point implementations. To achieve better accuracy we propose the use of the Kahan summation formula in matrix multiplications. While this drops overall performance, we demonstrate that the proposed new algorithm can match CPU single precision. (c) 2007 Elsevier B.V. All rights reserved.

关键词： graphical processing units quantum Monte Carlo matrix multiplication floating point error Kahan summation formula de-normals

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Acceleration of the GAMESS-UK Electronic Structure Package on graphical processing units

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2011年第10期32卷 2313-2318页

作者： Wilkinson, Karl A. Sherwood, Paul Guest, Martyn F. Naidoo, Kevin J. Univ Cape Town Sci Comp Res Unit ZA-7925 Cape Town South Africa Univ Cape Town Dept Chem ZA-7925 Cape Town South Africa STFC Daresbury Lab Warrington WA4 4AD Cheshire England Cardiff Univ ARCCA Cardiff CF10 3NB S Glam Wales

The approach used to calculate the two-electron integral by many electronic structure packages including generalized atomic and molecular electronic structure system-UK has been designed for CPU-based compute units. We redesigned the two-electron compute algorithm for acceleration on a graphical processing unit (GPU). We report the acceleration strategy and illustrate it on the (ss vertical bar ss) type integrals. This strategy is general for Fortran-based codes and uses the Accelerator compiler from Portland Group International and GPU-based accelerators from Nvidia. The evaluation of (ss vertical bar ss) type integrals within calculations using Hartree Fock ab initio methods and density functional theory are accelerated by single and quad GPU hardware systems by factors of 43 and 153, respectively. The overall speedup for a single self consistent field cycle is at least a factor of eight times faster on a single GPU compared with that of a single CPU. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 2313-2318, 2011

关键词： graphical processing units two-electron repulsion integrals fortran scientific computing accelerated computing heterogeneous computing GAMESS GAMESS-UK

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Fast computation of scattering maps of nanostructures using graphical processing units

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2011年第3期44卷 635-640页

作者： Favre-Nicolin, Vincent Coraux, Johann Richard, Marie-Ingrid Renevier, Hubert Univ Grenoble 1 CEA INAC SP2M Grenoble France Univ Grenoble 1 CNRS Inst Neel Grenoble France Univ Aix Marseille IM2NP CNRS FSTUMR 6242 Marseille France MINATEC Grenoble INP Mat & Genie Phys Lab Grenoble France

Scattering maps from strained or disordered nanostructures around a Bragg reflection can be either computed quickly using approximations and a (fast) Fourier transform or obtained using individual atomic positions. In this article, it is shown that it is possible to compute up to 4 x 10(10) reflections atoms s (1) using a single graphics card, and the manner in which this speed depends on the number of atoms and points in reciprocal space is evaluated. An open-source software library (PyNX) allowing easy scattering computations (including grazing-incidence conditions) in the Python language is described, with examples of scattering from non-ideal nanostructures.

关键词： X-ray scattering nanostructures grazing-incidence diffraction distorted-wave Born approximation DWBA coherent diffraction graphical processing units GPU

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Solving longest common subsequence and related problems on graphical processing units

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SOFTWARE-PRACTICE & EXPERIENCE 2010年第8期40卷 673-700页

作者： Deorowicz, Sebastian Silesian Tech Univ PL-44100 Gliwice Poland

Modern graphical processing units (GPUs) offer much more computational power than modern central processing units. Therefore, it is natural that GPUs are applied not only for their original purposes, but also for general processing (GPGPU). In the field of sequence processing, one of the most important problems is the measuring of sequence similarity. There are many sequence similarity measures, e.g. edit distance, longest common subsequence length, and their derivatives. We examine the possibility of speeding up the algorithms computing some of them. We chose three measures useful in different situations. The experimental results show that the GPU versions of the examined algorithms are faster than their serial counterparts by a factor between 4 and 65. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： longest common subsequence constrained longest common subsequence transposition invariance graphical processing units general processing using GPU CUDA

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MHD code using multi graphical processing units: SMAUG

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ADVANCES IN SPACE RESEARCH 2018年第2期61卷 683-690页

作者： Gyenge, N. Griffiths, M. K. Erdelyi, R. Univ Sheffield Sch Math & Stat SP2RC Solar Phys & Space Plasmas Res Ctr Hounsfield Rd Sheffield S3 7RH S Yorkshire England Hungarian Acad Sci Res Ctr Astron & Earth Sci Debrecen Heliophys Observ DHO Konkoly Observ POB 30 H-4010 Debrecen Hungary Univ Sheffield Corp Informat & Comp Serv 10-12 Brunswick St Sheffield S10 2FN S Yorkshire England Eotvos Lorand Univ Dept Astron H-1518 Budapest Hungary

This paper introduces the Sheffield Magnetohydrodynamics Algorithm Using GPUs (SMAUG+), an advanced numerical code for solving magnetohydrodynamic (MHD) problems, using multi-GPU systems. Multi-GPU systems facilitate the development of accelerated codes and enable us to investigate larger model sizes and/or more detailed computational domain resolutions. This is a significant advancement over the parent single-GPU MHD code, SMAUG (Griffiths et al., 2015). Here, we demonstrate the validity of the SMAUG + code, describe the parallelisation techniques and investigate performance benchmarks. The initial configuration of the Orszag-Tang vortex simulations are distributed among 4, 16, 64 and 100 GPUs. Furthermore, different simulation box resolutions are applied: 1000 x 1000, 2044 x 2044, 4000 x 4000 and 8000 x 8000. We also tested the code with the Brio-Wu shock tube simulations with model size of 800 employing up to 10 GPUs. Based on the test results, we observed speed ups and slow downs, depending on the granularity and the communication overhead of certain parallel tasks. The main aim of the code development is to provide massively parallel code without the memory limitation of a single GPU. By using our code, the applied model size could be significantly increased. We demonstrate that we are able to successfully compute numerically valid and large 2D MHD problems. (C) 2017 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Numerical simulations Magnetohydrodynamics graphical processing units Sheffield advanced code

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Accelerating Correlated Quantum Chemistry Calculations Using graphical processing units

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COMPUTING IN SCIENCE & ENGINEERING 2010年第4期12卷 40-50页

作者： Watson, Mark A. Olivares-Amaya, Roberto Edgar, Richard G. Arias, Tomas Aspuru-Guzik, Alan Harvard Univ Dept Chem & Chem Biol Cambridge MA 02138 USA Harvard Univ Initiat Innovat Comp Cambridge MA 02138 USA Cornell Univ Dept Phys Ithaca NY 14853 USA

graphical processing units are now being used with dramatic effect to accelerate quantum chemistry applications. The authors give a brief introduction to electronic structure methods and describe their efforts to accelerate a correlated quantum chemistry code. They propose and analyze two new tools for accelerating matrix-multiplications where single-precision accuracy is insuffcient.

关键词： chemistry graphical processing units quantum calculations

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A Fast MHD Code for Gravitationally Stratified Media using graphical processing units: SMAUG

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JOURNAL OF ASTROPHYSICS AND ASTRONOMY 2015年第1期36卷 197-223页

作者： Griffiths, M. K. Fedun, V. Erdelyi, R. Univ Sheffield Corp Informat & Comp Serv Sheffield S10 2FN S Yorkshire England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Univ Sheffield Sch Math & Stat SP2RC Sheffield S7 3RH S Yorkshire England

Parallelization techniques have been exploited most successfully by the gaming/graphics industry with the adoption of graphical processing units (GPUs), possessing hundreds of processor cores. The opportunity has been recognized by the computational sciences and engineering communities, who have recently harnessed successfully the numerical performance of GPUs. For example, parallel magnetohydrodynamic (MHD) algorithms are important for numerical modelling of highly inhomogeneous solar, astrophysical and geophysical plasmas. Here, we describe the implementation of SMAUG, the Sheffield Magnetohydrodynamics Algorithm Using GPUs. SMAUG is a 1-3D MHD code capable of modelling magnetized and gravitationally stratified plasma. The objective of this paper is to present the numerical methods and techniques used for porting the code to this novel and highly parallel compute architecture. The methods employed are justified by the performance benchmarks and validation results demonstrating that the code successfully simulates the physics for a range of test scenarios including a full 3D realistic model of wave propagation in the solar atmosphere.

关键词： Numerical simulations magnetohydrodynamics computer unified device architecture graphical processing units NVIDIA Sheffield advanced code the Sheffield magnetohydrodynamics algorithm using GPUs versatile advection code

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An Experimental Evaluation Of Probabilistic Deep Networks For Real-Time Traffic Scene Representation Using graphical processing units

An Experimental Evaluation Of Probabilistic Deep Networks Fo...

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作者： Mennat Allah El-Shaer The Ohio State University

学位级别：博士

The problem of scene understanding and environment perception has been an importantone in robotics research, however existing solutions applied in current Advanced DrivingAssistance systems (ADAS) are not robust enough to ensure the safety of traffic participants. ADAS development begins with sensor data collection and algorithms that can interpret that data to guide the intelligent vehicle's control decisions. Much work has been done to extract information from camera based image sensors, however most solutions require hand-designed features that usually break down under different lighting and weather *** traffic scenes, in particular, present a challenge to vision perception systems dueto the dynamic interactions among participants whether they are pedestrians, bicyclists, or other vehicles. Object detection deep learning models have proved successful in classifying or identifying objects on the road, but do not allow for the probabilistic reasoning and learning that traffic situations require. Deep Generative Models that learn the data distribution of training sets are capable of generating samples from the trained model that better represent sensory data, which leads to better feature representations and eventually better perception systems. Learning such models is computationally intensive so we decide to utilize Graphics processing chips designed for vision processing. In this thesis, we present a small image dataset collected from different types of busy intersections on a university campus along with our CUDA implementations of training a Restricted Boltzmann Machine on NVIDIA GTX1080 GPU, and its generative sampling inference on an NVIDIA Tegra X1 SoC *** demonstrate the sampling capability of a simple unsupervised network trained on asubset of the dataset, along with proling results from experiments done on the Jetson TX1 platform. We also include a quantitative study of different GPU optimization techniques performed on the Jetson TX1.

关键词： Artificial Intelligence Computer Engineering Computer Science Electrical Engineering deep learning probabilistic graphical models traffic scene perception graphical processing units GPU autonomous vehicles

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Interactive Quantum Chemistry Enabled by Machine Learning, graphical processing units, and Cloud Computing

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ANNUAL REVIEW OF PHYSICAL CHEMISTRY 2023年第1期74卷 313-336页

作者： Raucci, Umberto Weir, Hayley Sakshuwong, Sukolsak Seritan, Stefan Hicks, Colton B. Vannucci, Fabio Rea, Francesco Martinez, Todd J. SLAC Natl Accelerator Lab Stanford PULSE Inst Menlo Pk CA 94025 USA Stanford Univ Dept Chem Stanford CA 94305 USA Italian Inst Technol Genoa Italy Stanford Univ Dept Management Sci & Engn Stanford CA USA Sandia Natl Labs Livermore CA USA Italian Inst Technol Genoa Italy

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units-accelerated quantum chemistry in the cloud, artificial intelligence-driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

关键词： interactive quantum chemistry natural user interfaces chemical structure recognition cloud computing graphical processing units extended reality

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GPUAnimats-Simulating animats, an agent-based, artificial life model on graphical processing units

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第24期32卷 e5968-e5968页

作者： Quach, Dara Q. Playne, Daniel P. Scogings, Chris J. Massey Univ Comp Sci POB 102-904North Shore Mail Ctr Auckland New Zealand

A parallel implementation of an agent-based, artificial life model for graphical processing units (GPUs) is presented. The model represents predator-prey populations as a collection of agents (animats) who act independently and interact with their environment and each other. Individual animat interactions include avoidance, mating, and hunting which contribute to emergent behavior in the system as a whole such as herd formation and defensive spirals. Computational simulations of the model exploring the behavior of the model are limited in size due to the large populations that may result from certain environments and rule combinations. Parallel methods for computing the model on GPUs and a CUDA implementation are presented and are shown to provide a significant performance improvement over existing sequential implementations without simplifying the model behavior. The reduced simulation run-time can allow both larger systems to be computed and a wider range of rule combinations that may produce larger populations to be investigated in a feasible amount of time.

关键词： agent-based model animats artificial life graphical processing units parallel computing

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