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Accelerating the scientific exploration process with scientific workflows

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Journal of Physics: Conference Series 2006年第1期46卷

作者： Ilkay Altintas Oscar Barney Zhengang Cheng Terence Critchlow Bertram Ludaescher Steve Parker Arie Shoshani Mladen Vouk San Diego Supercomputer Center University of California San Diego USA Scientific Computing and Imaging Institute University of Utah USA Department of Computer Science North Carolina State University USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA Department of Computer Science University of California Davis USA Computing Research Division Lawrence Berkeley National Laboratory USA

Although an increasing amount of middleware has emerged in the last few years to achieve remote data access, distributed job execution, and data management, orchestrating these technologies with minimal overhead still remains a difficult task for scientists. scientific workflow systems improve this situation by creating interfaces to a variety of technologies and automating the execution and monitoring of the workflows. Workflow systems provide domain-independent customizable interfaces and tools that combine different tools and technologies along with efficient methods for using them. As simulations and experiments move into the petascale regime, the orchestration of long running data and compute intensive tasks is becoming a major requirement for the successful steering and completion of scientific investigations. A scientific workflow is the process of combining data and processes into a configurable, structured set of steps that implement semi-automated computational solutions of a scientific problem. Kepler is a cross-project collaboration, co-founded by the SciDAC scientific Data Management (SDM) Center, whose purpose is to develop a domain-independent scientific workflow system. It provides a workflow environment in which scientists design and execute scientific workflows by specifying the desired sequence of computational actions and the appropriate data flow, including required data transformations, between these steps. Currently deployed workflows range from local analytical pipelines to distributed, high-performance and high-throughput applications, which can be both data- and compute-intensive. The scientific workflow approach offers a number of advantages over traditional scripting-based approaches, including ease of configuration, improved reusability and maintenance of workflows and components (called actors), automated provenance management, ''smart'' re-running of different versions of workflow instances, on-the-fly updateable parameters, monitoring

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A Lightweight Feature Selection Method Based on Rankability

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SSRN

SSRN 2023年

作者： Kong, Lingping Silva, Juan Domingo Velasquez Perfilieva, Irina Pant, Millie Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile University Hospital Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Instituto Sistemas Complejos de Ingeniería Santiago Chile Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. Data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely *** proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator *** experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

Efficient exascale discretizations: High-order finite element methods

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arXiv 2021年

作者： Kolev, Tzanio Fischer, Paul Min, Misun Dongarra, Jack Brown, Jed Dobrev, Veselin Warburton, Tim Tomov, Stanimire Shephard, Mark S. Abdelfattah, Ahmad Barra, Valeria Beams, Natalie Camier, Jean-Sylvain Chalmers, Noel Dudouit, Yohann Karakus, Ali Karlin, Ian Kerkemeier, Stefan Lan, Yu-Hsiang Medina, David Merzari, Elia Obabko, Aleksandr Pazner, Will Rathnayake, Thilina Smith, Cameron W. Spies, Lukas Swirydowicz, Kasia Thompson, Jeremy Tomboulides, Ananias Tomov, Vladimir Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Mathematics and Computer Science Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Innovative Computing Laboratory University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Colorado BoulderCO80309 United States Scientific Computation Research Center Rensselaer Polytechnic Institute TroyNY12180 United States Department of Mathematics Virginia Tech BlacksburgVA24061 United States AMD Research AustinTX78735 United States Mechanical Engineering Department Middle East Technical University Ankara06800 Turkey Occalytics LLC WeehawkenNJ07086 United States Department of Nuclear Engineering Penn StatePA16802 United States Pacific Northwest National Laboratory WA99352 United States

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially-assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Data visualization

EXPLICIT RUNGE–KUTTA METHODS THAT ALLEVIATE ORDER REDUCTION

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arXiv 2023年

作者： Biswas, Abhijit Ketcheson, David I. Roberts, Steven Seibold, Benjamin Shirokoff, David Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Mathematics Temple University PhiladelphiaPA19122 United States Department of Mathematical Sciences New Jersey Institute of Technology NewarkNJ07102 United States

Explicit Runge–Kutta (RK) methods are susceptible to a reduction in the observed order of convergence when applied to initial-boundary value problem with time-dependent boundary conditions. We study conditions on explicit RK methods that guarantee high-order convergence for linear problems;we refer to these conditions as weak stage order conditions. We prove a general relationship between the method’s order, weak stage order, and number of stages. We derive explicit RK methods with high weak stage order and demonstrate, through numerical tests, that they avoid the order reduction phenomenon up to any order for linear problems and up to order three for nonlinear *** Codes 65L04, 65L20, 65M12 Copyright © 2023, The Authors. All rights reserved.

关键词： Runge Kutta methods

Correction to “The Open Catalyst 2020 (OC20) Dataset and Community Challenges”

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ACS Catalysis 2021年第21期11卷 13062-13065页

作者： Lowik Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi Morgane Riviere Kevin Tran Javier Heras-Domingo Caleb Ho Weihua Hu Aini Palizhati Anuroop Sriram Brandon Wood Junwoong Yoon Devi Parikh C. Lawrence Zitnick Zachary Ulissi Facebook AI Research (FAIR) 1 Hacker Way Menlo Park California 94025 United States Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Computer Science Department Stanford University Stanford California 94305 United States National Energy Research Scientific Computing Center (NERSC) Lawrence Berkeley National Laboratory Berkeley California 94720 United States School of Interactive Computing Georgia Tech Atlanta Georgia 30332 United States Scott Institute for Energy Innovation Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States

Link Prediction Accuracy on Real-World Networks Under Non-Uniform Missing Edge Patterns

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arXiv 2024年

作者： He, Xie Ghasemian, Amir Lee, Eun Schwarze, Alice C. Clauset, Aaron Mucha, Peter J. The Department of Mathematics Dartmouth College HanoverNH03755 United States The Human Nature Lab Yale University CT06520 United States The Computational Social Science Lab The University of Pennsylvania PA19104 United States The Department of Scientific Computing Pukyong National University Busan Korea Republic of The Department of Computer Science The BioFrontiers Institute The University of Colorado BoulderCO80309 United States The Santa Fe Institute Santa FeNM87501 United States

Real-world network datasets are typically obtained in ways that fail to capture all edges. The patterns of missing data are often non-uniform as they reflect biases and other shortcomings of different data collection methods. Nevertheless, uniform missing data is a common assumption made when no additional information is available about the underlying missing-edge pattern, and link prediction methods are frequently tested against uniformly missing edges. To investigate the impact of different missing-edge patterns on link prediction accuracy, we employ 9 link prediction algorithms from 4 different families to analyze 20 different missing-edge patterns that we categorize into 5 groups. Our comparative simulation study, spanning 250 real-world network datasets from 6 different domains, provides a detailed picture of the significant variations in the performance of different link prediction algorithms in these different settings. With this study, we aim to provide a guide for future researchers to help them select a link prediction algorithm that is well suited to their sampled network data, considering the data collection process and application domain. © 2024, CC BY.

关键词： Data acquisition

A Stefan problem related to a non-equilibrium shrinking unreacted core model with fast reaction for the sphere

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PAMM 2008年第1期7卷

作者： Adrian Muntean CASA – Center for Analysis Scientific computing and Applications Department of Mathematics and Computer Science TU Eindhoven PO Box 513 5600 MB Eindhoven The Netherlands

The problem studied in this note refers to a substantial part of a larger system of partial differential equations modeling diffusion and fast reaction of a gaseous species A in a reactive spherical porous region. We report on the local existence and uniqueness of weak solutions to the corresponding moving-boundary system containing two coupled mass-balance equations in a moving annulus. Since the system incorporates an explicit description of the velocity of the moving boundary, our formulation resembles to the one-phase Stefan-like problem with kinetic condition. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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A complete flux scheme for the stationary advection-diffusion-reaction equation

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PAMM 2008年第1期7卷

作者： J.H.M. ten Thije Boonkkamp Centre for Analysis Scientific Computing and Applications Department of Mathematics and Computer Science Eindhoven University of Technology PO Box 513 5600 MB Eindhoven The Netherlands

Expressions for the numerical flux of a conservation law of advection-diffusion-reaction type are derived from a local solution of the entire conservation law, including the source term. The resulting complete flux scheme is given for one-dimensional (in Cartesian and spherical coordinates) and two-dimensional model equations. Combined with a finite volume method, the numerical scheme is second order accurate, uniformly in the Peclet numbers. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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How quantum and evolutionary algorithms can help each other: two examples

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arXiv 2024年

作者： Bhandari, Shailendra Nichele, Stefano Denysov, Sergiy Lind, Pedro G. Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass OsloN-0130 Norway OsloMet Artificial Intelligence Lab Pilestredet 52 OsloN-0166 Norway NordSTAR - Nordic Center for Sustainable and Trustworthy AI Research Pilestredet 52 OsloN-0166 Norway Department of Computer Science and Communication Østfold University College B R A Veien 4 HaldenN-1757 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochastic cellular automata with given rules. We test the robustness of quantum implementations of the cellular automata for different numbers of quantum gates The second task deals with the sampling of quantum circuits that generate highly entangled quantum states, which constitute an important resource for quantum computing. In particular, an evolutionary algorithm is employed to optimize circuits with respect to a fitness function defined with the Mayer-Wallach entanglement measure. We demonstrate that, by balancing the mutation rate between exploration and exploitation, we can find entangling quantum circuits for up to five qubits. We also discuss the trade-off between the number of gates in quantum circuits and the computational costs of finding the gate arrangements leading to a strongly entangled state. Our findings provide additional insight into the trade-off between the complexity of a circuit and its performance, which is an important factor in the design of quantum circuits. © 2024, CC BY.

关键词： Quantum computers