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A Smart Forecasting System of Computational Cost of DFT calculations using Machine Learning and Cheminformatics

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A Smart Forecasting System of Computational Cost of DFT calc...

2019中国化学会第十五届全国计算（机）化学学术会议

作者：马硕 Yingjin Ma Baohua Zhang Yingqi Tian Zhong Jin 中科院计算机网络信息中心 Department of High Performance Computing Technology and Application Development Computer Network Information Center Chinese Academy of Sciences Center of Scientific Computing Applications & Research Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

The Density-functional theory(DFT) should be the most popular approach that used in computational physics or computational chemistry community. Normally, the computational time is unknown until the calculation is done. And as such, researchers can hardly arrange their schedules rationally. Additionally, for calculations that are carried out with computing clusters, unreasonable schedules would greatly increase the time and energy cost. Herein, we introduced a smart forecasting system based on three machine learning models, which are RF with FNNs(random forest with feedforward neural networks), Bi-LSTM with attention mechanism[1,2,3] and MPNN[4](message passing neural networks). The three models can accept the molecular information, calculating scheme(the combination of basis set and functional etc.) and machine configuration information as inputs, and then can predict the proposed computational cost. Comparing to the actual computational time, the average relative error is generally less than 20%, and it follows the normal distribution[5].

关键词： density-functional theory machine learning neural networks cheminformatics

Simulation-Based Performance Analysis and Tuning for a Two-Level Directly Connected System

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Simulation-Based Performance Analysis and Tuning for a Two-L...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ehsan Totoni Abhinav Bhatele Eric J. Bohm Nikhil Jain Celso L. Mendes Ryan M. Mokos Gengbin Zheng Laxmikant V. Kale Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA Center of Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA National Center of Supercomputing Applications University of Illinois Urbana IL USA

Hardware and software co-design is becoming increasingly important due to complexities in supercomputing architectures. Simulating applications before there is access to the real hardware can assist machine architects in making better design decisions that can optimize application performance. At the same time, the application and runtime can be optimized and tuned beforehand. BigSim is a simulation-based performance prediction framework designed for these purposes. It can be used to perform packet-level network simulations of parallel applications using existing parallel machines. In this paper, we demonstrate the utility of BigSim in analyzing and optimizing parallel application performance for future systems based on the PERCS network. We present simulation studies using benchmarks and real applications expected to run on future supercomputers. Future petascale systems will have more than 100,000 cores, and we present simulations at that scale.

关键词： Noise Topology Tuning Network topology Routing Supercomputers Three dimensional displays

Multi-reference Epstein-Nesbet perturbation theory with density matrix renormalization group reference wavefunction

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arXiv 2019年

作者： Song, Yinxuan Cheng, Yifan Ma, Yingjin Ma, Haibo School of Chemistry and Chemical Engineering Nanjing University Nanjing210023 China Department of High Performance Computing Technology and Application Development Computer Network Information Center Chinese Academy of Sciences Beijing100190 China Center of Scientific Computing Applications & Research Chinese Academy of Sciences Beijing100190 China

The accurate electronic structure calculation for strongly correlated chemical systems requires an adequate description for both static and dynamic electron correlation, and is a persistent challenge for quantum chemistry. In order to account for static and dynamic electron correlations accurately and efficiently, in this work we propose a new method by integrating the density matrix renormalization group (DMRG) method and multi-reference second-order Epstein-Nesbet perturbation theory (ENPT2) with a selected configuration interaction (SCI) approximation. Compared to previous DMRG-based dynamic correlation methods, the DMRG-ENPT2 method extends the range of applicability, allowing us to efficiently calculate systems with very large active spaces beyond 30 orbitals. We demonstrate this by performing calculations on H2S with an active space of (16e, 15o), hexacene with an active space of (26e, 26o) and trinuclear Manganese cluster with an active space of (47e, 43o). Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum chemistry

A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

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A Toolbox of Feminist Wonder: Theories and methods that can ...

26th ACM Conference on computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

Position-Dependent Snap Feedforward: A Gaussian Process Framework

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arXiv 2022年

作者： van Haren, Max Poot, Maurice Portegies, Jim Oomen, Tom The Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands The Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e. the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward parameter to be modeled as a function of position. Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position. A simulation of a flexible beam shows that a significant performance increase is achieved when using the Gaussian process snap feedforward parameter to compensate for position-dependent compliance. © 2022, CC BY.

关键词： Flexible structures

Analytical gradients and nonadiabatic couplings for the state-average density matrix renormalization group self-consistent field method

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arXiv 2019年

作者： Freitag, Leon Ma, Yingjin Baiardi, Alberto Knecht, Stefan Reiher, Markus ETH Zürich Laboratorium für Physikalische Chemie Vladimir-Prelog-Weg 2 Zürich8093 Switzerland Department of High Performance Computing Computer Network Information Center Chinese Academy of Sciences Beijing100190 China Center of Scientific Computing Applications & Research Chinese Academy of Sciences Beijing100190 China

We present analytical gradients and nonadiabatic couplings for a state-average density matrix renormalization group self-consistent-field (SA-DMRG-SCF) wavefunction. Our formalism follows closely the state-average complete active space self-consistent-field (SA-CASSCF) ansatz, which employs a Lagrangian, and the corresponding Lagrange multipliers are obtained from a solution of the coupled-perturbed CASSCF (CP-CASSCF) equations. We introduce a definition of the MPS Lagrange multipliers based on the mixed-canonical form of the MPS, such that the sweep procedure is avoided in the solution of the CP-CASSCF equations. We employ our implementation for the optimization of a conical intersection in 1,2-dioxetanone, where we are able to fully reproduce the SA-CASSCF result up to arbitrary accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Lagrange multipliers

Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: Extension to geometrical parameterizations

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arXiv 2022年

作者： Guo, Theron Silva, Francesco A.B. Rokoš, Ondřej Veroy, Karen Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5612 AZ Netherlands Mechanics of Materials Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven5612 AZ Netherlands

Two-scale simulations are often employed to analyze the effect of the microstructure on a component's macroscopic properties. Understanding these structure-property relations is essential in the optimal design of materials for specific applications. However, these two-scale simulations are typically computationally expensive and infeasible in multi-query contexts such as optimization and material design. To make such analyses amenable, the microscopic simulations can be replaced by inexpensive-to-evaluate surrogate models. Such surrogate models must be able to handle microstructure parameters in order to be used for material design. A previous work focused on the construction of an accurate surrogate model for microstructures under varying loading and material parameters by combining proper orthogonal decomposition and Gaussian process regression. However, that method works only for a fixed geometry, greatly limiting the design space. This work hence focuses on extending the methodology to treat geometrical parameters. To this end, a method that transforms different geometries onto a parent domain is presented, that then permits existing methodologies to be applied. We propose to solve an auxiliary problem based on linear elasticity to obtain the geometrical transformations. The method has a good reducibility and can therefore be quickly solved for many different geometries. Using these transformations, combined with the nonlinear microscopic problem, we derive a fast-to-evaluate surrogate model with the following key features: (1) the predictions of the effective quantities are independent of the auxiliary problem, (2) the predicted stress fields automatically fulfill the microscopic balance laws and are periodic, (3) the method is non-intrusive, (4) the stress field for all geometries can be recovered, and (5) the sensitivities are available and can be readily used for optimization and material design. The proposed methodology is tested on several composite micros

关键词： Geometry

Externally-contracted multi-reference configuration interaction method using a DMRG reference wave function

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arXiv 2018年

作者： Luo, Zhen Ma, Yingjin Wang, Xicun Ma, Haibo School of Chemistry and Chemical Engineering Nanjing University Nanjing210023 China Department of High Performance Computing Technology and Application Development Computer Network Information Center Chinese Academy of Sciences Beijing100190 China Center of Scientific Computing Applications & Research Chinese Academy of Sciences Beijing100190 China Nanjing Foreign Language School Nanjing210008 China

The recent development of the density matrix renormalization group (DMRG) method in multireference quantum chemistry makes it practical to evaluate static correlation in a large active space, while dynamic correlation provides a critical correction to the DMRG reference for strong-correlated systems and is usually obtained using multi-reference perturbation (MRPT) or configuration interaction (MRCI) methods with internal contraction (ic) approximation. These methods can use active space scalable to relatively larger size references than has previously been possible. However, they are still hardly applicable to systems with active space larger than 30 orbitals and/or large basis set because of high computation and storage costs of high-order reduced density matrices (RDMs) and the crucial dependence of the MRCI Hamiltonian dimension on the number of virtual orbitals. In this work, we propose a new effective implementation of DMRG-MRCI, in which we use re-constructed CASCI-type configurations from DMRG wave function via the entropy-driving genetic algorithm (EDGA) [Luo et. al., J. Chem. Theory Comput., 2017, 13, 4699-4710.], and integrate it with MRCI by an external contraction (ec) scheme. This bypasses the bottleneck of computing high-order RDMs in traditional DMRG dynamic correlation methods with ic approximation and the number of MRCI configurations is not dependent on the number of virtual orbitals. Therefore, DMRG-ec-MRCI method is promising for dealing with larger active space than 30 orbitals and large basis sets. We demonstrate the capability of our DMRG-ec-MRCI method in several benchmark applications, including the evaluation of potential energy curve of Cr2, single-triplet gaps of higher n-acene molecules and the energy of Eu-BTBP(NO3)3 complex. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum chemistry

SSC: Cloud-based data stream management in distributed environments

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International Journal of High Performance computing and Networking 2016年第3期9卷 171-189页

作者： Ong, Kok-Leong Goscinski, Andrzej Han, Yuzhang Brezany, Peter Tari, Zahir Yan, Lei La Trobe Business School La Trobe University Australia School of Information Technology Deakin University Australia Department of Computer Science Duke University DurhamNC United States Research Group Scientific Computing Faculty of Computer Science University of Vienna Nordbergstrasse 15/C/315 Vienna Austria School of Computer Science RMIT University Australia Spatial Information Integration and Its Applications Laboratory Peking University Beijing China

Stream-oriented applications account for one of the major types of today's computing practices. They deal with high-speed data streams. When it comes to stream-oriented distributed systems, some challenges arise: for one thing, processing capability of a single compute node might become the bottleneck of the entire system. For another, transmitting streams among physically dispersed nodes could incur both communication overhead and instability of a distributed application. In this paper, we discuss the potential of using cloud technology to respond to various challenges faced by stream-oriented distributed systems. We introduce our idea of a special cloud, the scientific streaming cloud (SSC), which supports stream management in distributed environments. We also present our design of a language and an algorithm which are used on the SSC to describe and optimise stream-related tasks, respectively. Furthermore, we demonstrate our prototype implementation and a real-world application of the SSC. © 2016 Inderscience Enterprises Ltd.

关键词： Environmental management