Pub Date : 2024-02-15DOI: 10.1109/mcse.2024.3357330
{"title":"IEEE Computer Society Call for Papers","authors":"","doi":"10.1109/mcse.2024.3357330","DOIUrl":"https://doi.org/10.1109/mcse.2024.3357330","url":null,"abstract":"","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"30 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/mcse.2023.3322127
Curran D. Muhlberger
Too often, reproducibility is unnecessarily sacrificed in new simulation codes. We explore some ways in which this happens and provide recommendations for reclaiming it. Experience shows that robust bitwise reproducibility on a fixed runtime platform is a desirable and achievable target. The variety of threats considered suggests that maintaining a reproducible simulator to this degree requires vigilance, but, in addition to the usual benefits, the increased effort is rewarded on the software engineering front by enabling low-overhead techniques to detect bugs sooner and diagnose them faster.
{"title":"Challenges and Techniques for Reproducible Simulations","authors":"Curran D. Muhlberger","doi":"10.1109/mcse.2023.3322127","DOIUrl":"https://doi.org/10.1109/mcse.2023.3322127","url":null,"abstract":"Too often, reproducibility is unnecessarily sacrificed in new simulation codes. We explore some ways in which this happens and provide recommendations for reclaiming it. Experience shows that robust bitwise reproducibility on a fixed runtime platform is a desirable and achievable target. The variety of threats considered suggests that maintaining a reproducible simulator to this degree requires vigilance, but, in addition to the usual benefits, the increased effort is rewarded on the software engineering front by enabling low-overhead techniques to detect bugs sooner and diagnose them faster.","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/mcse.2024.3357309
{"title":"IEEE Computer Society Career Center","authors":"","doi":"10.1109/mcse.2024.3357309","DOIUrl":"https://doi.org/10.1109/mcse.2024.3357309","url":null,"abstract":"","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"2 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/mcse.2023.3326436
Philip Carns, Matthieu Dorier, Rob Latham, Robert B. Ross, Shane Snyder, Jerome Soumagne
High-performance computing (HPC) has become an indispensable tool for solving diverse problems in science and engineering. Harnessing the power of HPC is not just a matter of efficient computation, however; it also calls for the efficient management of vast quantities of scientific data. This presents daunting challenges: rapidly evolving storage technology has motivated a shift toward increasingly complex, heterogeneous storage architectures that are difficult to optimize, and scientific data management needs have become every bit as diverse as the application domains that drive them. There is a clear need for agile, adaptable storage solutions that can be customized for the task and platform at hand. This motivated the establishment of the Mochi composable data service project. The Mochi project provides a library of robust, reusable, modular, and connectable data management components and microservices along with a methodology for composing them into specialized distributed data services. Mochi enables rapid deployment of custom data services with a high degree of developer productivity while still effectively leveraging cutting-edge HPC hardware. This article explores how the principles of translational computer science have been applied in practice in Mochi to achieve these goals.
{"title":"Mochi: A Case Study in Translational Computer Science for High-Performance Computing Data Management","authors":"Philip Carns, Matthieu Dorier, Rob Latham, Robert B. Ross, Shane Snyder, Jerome Soumagne","doi":"10.1109/mcse.2023.3326436","DOIUrl":"https://doi.org/10.1109/mcse.2023.3326436","url":null,"abstract":"High-performance computing (HPC) has become an indispensable tool for solving diverse problems in science and engineering. Harnessing the power of HPC is not just a matter of efficient computation, however; it also calls for the efficient management of vast quantities of scientific data. This presents daunting challenges: rapidly evolving storage technology has motivated a shift toward increasingly complex, heterogeneous storage architectures that are difficult to optimize, and scientific data management needs have become every bit as diverse as the application domains that drive them. There is a clear need for agile, adaptable storage solutions that can be customized for the task and platform at hand. This motivated the establishment of the Mochi composable data service project. The Mochi project provides a library of robust, reusable, modular, and connectable data management components and microservices along with a methodology for composing them into specialized distributed data services. Mochi enables rapid deployment of custom data services with a high degree of developer productivity while still effectively leveraging cutting-edge HPC hardware. This article explores how the principles of translational computer science have been applied in practice in Mochi to achieve these goals.","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"214 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/mcse.2023.3332321
William J. Harrod
New data-centric architectures optimized for knowledge discovery and analytics are urgently required. This article describes the Intelligence Advanced Research Projects Activity’s Advanced Graph Intelligent Logical Computing Environment program, the first step toward catalyzing a computing revolution by pioneering new hardware and software co-designs tailored for data handling and movement. The goal is to empower transformative applications across all fields through efficient, scalable systems balanced for both data-intensive and compute-intensive workloads. Realizing this vision demands continued research into components, prototypes, and architectural design principles that place priority on the data.
{"title":"The Intelligence Advanced Research Projects Activity Advanced Graph Intelligent Logical Computing Environment Program: Reinventing Computing","authors":"William J. Harrod","doi":"10.1109/mcse.2023.3332321","DOIUrl":"https://doi.org/10.1109/mcse.2023.3332321","url":null,"abstract":"New data-centric architectures optimized for knowledge discovery and analytics are urgently required. This article describes the Intelligence Advanced Research Projects Activity’s Advanced Graph Intelligent Logical Computing Environment program, the first step toward catalyzing a computing revolution by pioneering new hardware and software co-designs tailored for data handling and movement. The goal is to empower transformative applications across all fields through efficient, scalable systems balanced for both data-intensive and compute-intensive workloads. Realizing this vision demands continued research into components, prototypes, and architectural design principles that place priority on the data.","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"3 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1109/mcse.2024.3357329
{"title":"IEEE Computer Society Has You Covered!","authors":"","doi":"10.1109/mcse.2024.3357329","DOIUrl":"https://doi.org/10.1109/mcse.2024.3357329","url":null,"abstract":"","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"154 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1109/mcse.2024.3362586
Ryan Adamson, Paul Bryant, Dave Montoya, Jeff Neel, Erik Palmer, Ray Powell, Ryan Prout, Peter Upton
{"title":"Creating Continuous Integration Infrastructure for Software Development on DOE HPC Systems","authors":"Ryan Adamson, Paul Bryant, Dave Montoya, Jeff Neel, Erik Palmer, Ray Powell, Ryan Prout, Peter Upton","doi":"10.1109/mcse.2024.3362586","DOIUrl":"https://doi.org/10.1109/mcse.2024.3362586","url":null,"abstract":"","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"51 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-26DOI: 10.1109/mcse.2023.3346208
Andrei Chertkov, Gleb Ryzhakov, Georgii Novikov, Ivan Oseledets
The tensor train (TT) format, widely used in computational mathematics and machine learning, offers a computationally efficient method for handling multidimensional arrays, vectors, matrices, and discretized functions in various applications. In this article, we propose a new algorithm for estimating minimum/maximum elements of TT-tensors, which leads to accurate approximations. The method consists of sequential tensor multiplications of the TT-cores with an intelligent selection of candidates for the optimum. We propose a probabilistic interpretation of the method and estimate its complexity and convergence. We perform extensive numerical experiments with random tensors and various multivariable benchmark functions with the number of input dimensions up to 100. Our approach generates a solution close to the exact optimum for all model problems on a regular laptop.
{"title":"Tensor Extrema Estimation Via Sampling: A New Approach for Determining Minimum/Maximum Elements","authors":"Andrei Chertkov, Gleb Ryzhakov, Georgii Novikov, Ivan Oseledets","doi":"10.1109/mcse.2023.3346208","DOIUrl":"https://doi.org/10.1109/mcse.2023.3346208","url":null,"abstract":"The tensor train (TT) format, widely used in computational mathematics and machine learning, offers a computationally efficient method for handling multidimensional arrays, vectors, matrices, and discretized functions in various applications. In this article, we propose a new algorithm for estimating minimum/maximum elements of TT-tensors, which leads to accurate approximations. The method consists of sequential tensor multiplications of the TT-cores with an intelligent selection of candidates for the optimum. We propose a probabilistic interpretation of the method and estimate its complexity and convergence. We perform extensive numerical experiments with random tensors and various multivariable benchmark functions with the number of input dimensions up to 100. Our approach generates a solution close to the exact optimum for all model problems on a regular laptop.","PeriodicalId":10553,"journal":{"name":"Computing in Science & Engineering","volume":"18 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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