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arXiv 2018年

作者： Kim, Jeongnim Baczewski, Andrew Beaudet, Todd D. Benali, Anouar Bennett, M. Chandler Berrill, Mark A. Blunt, Nick S. Borda, Edgar Casula, Michele Ceperley, David M. Chiesa, Simone Clark, Bryan K. Clay, Raymond C. Delaney, Kris T. Dewing, Mark Esler, Kenneth P. Hao, Hongxia Heinonen, Olle Kent, Paul R.C. Krogel, Jaron T. Kylänpää, Ilkka Li, Ying Wai Lopez, M. Graham Luo, Ye Malone, Fionn D. Martin, Richard M. Mathuriya, Amrita McMinis, Jeremy Melton, Cody A. Mitas, Lubos Morales, Miguel A. Neuscamman, Eric Parker, William D. Flores, D. Pineda Romero, Nichols A. Rubenstein, Brenda M. Shea, Jacqueline A.R. Shin, Hyeondeok Shulenburger, Luke Tillack, Andreas Townsend, Joshua P. Tubman, Norm M. Goetz, Brett Vincent, Jordan E. Yang, D. ChangMo Yang, Yubo Zhang, Shuai Zhao, Luning Intel Corporation HillsboroOR987124 United States HEDP Theory Department Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Computational Science Division Argonne National Laboratory ArgonneIL60439 United States Department of Physics North Carolina State University RaleighNC27695 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States University Chemical Laboratory Lensfield Road CambridgeCB2 1EW United Kingdom Lawrence Livermore National Laboratory LivermoreCA94550 United States Sorbonne Université CNRS UMR 7590 IRD UMR 206 MNHN 4 Place Jussieu Paris75252 France Department of Physics University of Illinois UrbanaIL61801 United States Materials Research Laboratory University of California Santa BarbaraCA93106 United States Stone Ridge Technology Bel AirMD21015 United States Department of Chemistry Brown University ProvidenceRI02912 United States Material Science Division Argonne National Laboratory ArgonneIL60439 United States Northwestern-Argonne Institute for Science and Engineering Northwestern University EvanstonIL60208 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge TN37831 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry University of California BerkeleyCA94720 United States Chemical Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of M

QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit (CPU) and graphical processing unit (GPU) systems. We detail the program’s capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://***. Copyright © 2018, The Authors. All rights reserved.

关键词： Electronic structure

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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A language and hardware independent approach to quantum-classical computing

arXiv

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arXiv 2017年

作者： McCaskey, A.J. Dumitrescu, E.F. Liakh, D. Chen, M. Feng, W. Humble, T.S. Quantum Computing Institute Oak Ridge National Laboratory Oak RidgeTN United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Oak Ridge Leadership Computing Facility Scientific Computing Oak Ridge National Laboratory Oak RidgeTN United States Department of Physics Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Bredesen Center for Interdisciplinary Research University of Tennessee KnoxvilleTN United States

Heterogeneous high-performance computing (HPC) systems offer novel architectures which accelerate specific workloads through judicious use of specialized coprocessors. A promising architectural approach for future scientific computations is provided by heterogeneous HPC systems integrating quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale ACCelerator) - a programming model and software framework that enables quantum acceleration within standard or HPC software workflows. XACC follows a coprocessor machine model that is independent of the underlying quantum computing hardware, thereby enabling quantum programs to be defined and executed on a variety of QPUs types through a unified application programming interface. Moreover, XACC defines a polymorphic low-level intermediate representation, and an extensible compiler frontend that enables language independent quantum programming, thus promoting integration and interoperability across the quantum programming landscape. In this work we define the software architecture enabling our hardware and language independent approach, and demonstrate its usefulness across a range of quantum computing models through illustrative examples involving the compilation and execution of gate and annealing-based quantum programs. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum computers

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Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

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2016年大连国际微流控和芯片实验室大会

作者： Chu Li Jizu Huang Zhigang Li Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of Sciences

In this study,we investigate how nanodroplets diffuse on smooth surfaces through molecular dynamics(MD) simulations and theoretical *** dynamics simulations show that nanodroplet surface diffusion is different from that of single molecules and solid *** dependence of nanodroplet diffusion coefficient on temperature undergoes a transition from linear to nonlinear as the surface wettability is weakened due to the coupling of temperature and surface *** also develop a simple relation for the diffusion coefficient by using the contact angle and contact radius of the *** works well for a wide range of surface wettabilities and different sized nanodroplets,as confirmed by MD simulations.

关键词： Track 1-A Study of Nanodroplet Diffusion on Smooth Surfaces

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Building a research data science platform from industrial machines

Building a research data science platform from industrial ma...

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IEEE International Conference on Big Data

作者： Fang Cherry Liu Fu Shen Duen Horng Chau Neil Bright Mehmet Belgin Partnership for An Advanced Computing Environment (PACE) Georgia Institute of Technology Atlanta Georgia School of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia

ISBN: (纸本)9781467390064

Data science research has a long history in academia which spans from large-scale data management, to data mining and data analysis using technologies from database management systems (DBMS's). While traditional HPC offers tools on leveraging existing technologies with data processing needs, the large volume of data and the speed of data generation pose significant challenges. Using the Hadoop platform and tools built on top of it drew immense interest from academia after it gained success in industry. Georgia institute of Technology received a donation of 200 compute nodes from Yahoo. Turning these industrial machines into a research Data science Platform (DSP) poses unique challenges, such as: nontrivial hardware design decisions, configuration tool choices, node integration into existing HPC infrastructure, partitioning resource to meet different application needs, software stack choices, etc. We have 40 nodes up and running, 24 running as a Hadoop and Spark cluster, 12 running as a HBase and OpenTSDB cluster, the others running as service nodes. We successfully tested it against Spark Machine Learning algorithms using a 88GB image dataset, Spark DataFrame and GraphFrame with a Wikipedia dataset, and Hadoop MapReduce wordcount on a 300GB dataset. The OpenTSDB cluster is for real-time time series data ingestion and storage for sensor data. We are working on bringing up more nodes. We share our first-hand experience gained in our journey, which we believe will benefit and inspire other academic institutions.

关键词： Hardware Sparks Digital signal processing Ecosystems Distributed databases Cloud computing

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How AD can help solve differential-algebraic equations

arXiv

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arXiv 2017年

作者： Pryce, John D. Nedialkov, Nedialko S. Tan, Guangning Li, Xiao Cardiff University School of Mathematics Senghennydd Rd CardiffCF24 4AG United Kingdom McMaster University Department of Computing & Software HamiltonL8S 4K1 Canada Massachusetts Institute of Technology Process Systems Engineering Laboratory Boston United States McMaster University School of Computational Science and Engineering HamiltonL8S 4K1 Canada

A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of so called index reduction or regularisation, to prepare them for numerical solution. This is often done with the help of a computer algebra system. We show in two significant cases that it can be done efficiently by pure algorithmic differentiation. The first is the Dummy Derivatives method;here we give a mainly theoretical description, with tutorial examples. The second is the solution of a mechanical system directly from its Lagrangian formulation. Here we outline the theory and show several non-trivial examples of using the "Lagrangian facility" of the Nedialkov-Pryce initial-value solver DAETS, namely: a spring-mass-multipendulum system;a prescribed-trajectory control problem;and long-time integration of a model of the outer planets of the solar system, taken from the DETEST testing package for ODE solvers. Copyright © 2017, The Authors. All rights reserved.

关键词： Differential equations

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Siesta: recent developments and applications

arXiv

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arXiv 2020年

作者： García, Alberto Papior, Nick Akhtar, Arsalan Artacho, Emilio Blum, Volker Bosoni, Emanuele Brandimarte, Pedro Brandbyge, Mads Cerdá, J.I. Corsetti, Fabiano Cuadrado, Ramón Dikan, Vladimir Ferrer, Jaime Gale, Julian García-Fernández, Pablo García-Suárez, V.M. García, Sandra Huhs, Georg Illera, Sergio Korytár, Richard Koval, Peter Lebedeva, Irina Lin, Lin López-Tarifa, Pablo Mayo, Sara G. Mohr, Stephan Ordejón, Pablo Postnikov, Andrei Pouillon, Yann Pruneda, Miguel Robles, Roberto Sánchez-Portal, Daniel Soler, Jose M. Ullah, Rafi Wen-zhe Yu, Victor Junquera, Javier BellaterraE-08193 Spain DTU Computing Center Technical University of Denmark 2800 Kgs Lyngby Denmark Catalan Institute of Nanoscience Nanotechnology - ICN2 CSIC and BIST Campus UAB Bellaterra08193 Spain CIC Nanogune BRTA Tolosa Hiribidea 76 San Sebastián20018 Spain Paseo Manuel de Lardizabal 4 Donostia-San Sebastian20018 Spain Ikerbasque Basque Foundation for Science Bilbao48011 Spain Theory of Condensed Matter Cavendish Laboratory University of Cambridge CambridgeCB3 0HE United Kingdom Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Chemistry Duke University DurhamNC27708 United States Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Instituto de Ciencia de Materiales de Madrid ICMM-CSIC Cantoblanco Madrid28049 Spain Department of Physics University of Oviedo Oviedo33007 Spain Nanomaterials and Nanotechnology Research Center CSIC - Universidad de Oviedo Oviedo33007 Spain School of Molecular and Life Sciences Curtin University PO Box U1987 PerthWA6845 Australia Departamento de Ciencias de la Tierra y Física de la Materia Condensada Universidad de Cantabria Cantabria Campus Internacional Avenida de los Castros s/n Santander39005 Spain Barcelona Supercomputing Center c/ Jordi Girona 29 Barcelona08034 Spain Department of Condensed Matter Physics Faculty of Mathematics and Physics Charles University Ke Karlovu 5 Praha 2121 16 Czech Republic Simune Atomistics S.L. Tolosa Hiribidea 76 Donostia-San Sebastian20018 Spain Department of Mathematics University of California BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Centro de Física de Materiales Centro Mixto CSIC-UPV/EHU Paseo Manuel de Lardizabal 5 Donostia-San Sebastian20018 Spain Departamento de Física de la Materia Condensada Universidad Autónoma de Madrid Madrid28049 Spain LCP-A2MC Université de Lor

A review of the present status, recent enhancements, and applicability of the SIESTA program is presented. Since its debut in the mid-nineties, SIESTA’s flexibility, efficiency and free distribution has given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of SIESTA combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here we describe the more recent implementations on top of that core scheme, which include: full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, DFT+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient Van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as WANNIER90 and the second-principles modelling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering SIESTA runs, and various postprocessing utilities. SIESTA has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low level libraries, as well as data standards and support for them, in particular the PSML definition and library for transferable pseudopotentials, and the interface to the ELSI library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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Zirconia and hafnia polymorphs-ground state structural properties from diffusion Monte Carlo

arXiv

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arXiv 2017年

作者： Shin, Hyeondeok Benali, Anouar Luo, Ye Crabb, Emily Lopez-Bezanilla, Alejandro Ratcliff, Laura E. Jokisaari, Andrea M. Heinonen, Olle Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Materials Imperial College London LondonSW7 2AZ United Kingdom Northwestern-Argonne Institute of Science and Engineering Northwestern University EvanstonIL60208 United States

Zirconia (zirconium dioxide) and hafnia (hafnium dioxide) are binary oxides used in a range of applications. Because zirconium and hafnium are chemically equivalent, they have three similar polymorphs, and it is important to understand the properties and energetics of these polymorphs. However, while density functional theory calculations can get the correct energetic ordering, the energy differences between polymorphs depend very much on the specific density functional theory approach, as do other quantities such as lattice constants and bulk modulus. We have used highly accurate quantum Monte Carlo simulations to model the three zirconia and hafnia polymorphs. We compare our results for structural parameters, bulk modulus, and cohesive energy with results obtained from density functional theory calculations. We also discuss comparisons of our results with existing experimental data, in particular for structural parameters where extrapolation to zero temperature can be attempted. We hope our results of structural parameters as well as for cohesive energy and bulk modulus can serve as benchmarks for density-functional theory based calculations and as a guidance for future experiments. Copyright © 2017, The Authors. All rights reserved.

关键词： Zirconia

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Union of Intersections (UoI) for interpretable data driven discovery and prediction

arXiv

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arXiv 2017年

作者： Bouchard, Kristofer E. Bujan, Alejandro F. Roosta-Khorasani, Farbod Ubaru, Shashanka Prabhat Snijders, Antoine M. Mao, Jian-Hua Chang, Edward F. Mahoney, Michael W. Bhattacharyya, Sharmodeep Biological Systems and Engineering Division Lawrence Berkeley National Laboratory Computational Research Division Lawrence Berkeley National Laboratory Redwood Center for Theoretical Neuroscience University of California at Berkeley Kavli Institute for Fundamental Neuroscience University of California at San Francisco Redwood Center for Theoretical Neuroscience University of California at Berkeley International Computer Science Institute Department of Statistics University of California at Berkeley Department of Computer Science and Engineering University of Minnesota-Twin Cities National Energy Research Super Computing Lawrence Berkeley National Laboratory Biological Systems and Engineering Division Lawrence Berkeley National Laboratory Biological Systems and Engineering Division Lawrence Berkeley National Laboratory UCSF Epilepsy Center University of California at San Francisco Department of Neurological Surgery University of California at San Francisco International Computer Science Institute Department of Statistics University of California at Berkeley Department of Statistics Oregon State University

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced model selection and estimation. Methods based on UoI perform model selection and model estimation through intersection and union operations, respectively. We show that UoI-based methods achieve low-variance and nearly unbiased estimation of a small number of interpretable features, while maintaining high-quality prediction accuracy. We perform extensive numerical investigation to evaluate a UoI algorithm (UoILasso) on synthetic and real data. In doing so, we demonstrate the extraction of interpretable functional networks from human electrophysiology recordings as well as accurate prediction of phenotypes from genotype-phenotype data with reduced features. We also show (with the UoIL1Logistic and UoICUR variants of the basic framework) improved prediction parsimony for classification and matrix factorization on several benchmark biomedical data sets. These results suggest that methods based on the UoI framework could improve interpretation and prediction in data-driven discovery across scientific fields. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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