Mochi: Composing Data Services for High-Performance Computing Environments

Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of...

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Published in:	Journal of computer science and technology Vol. 35; no. 1; pp. 121 - 144
Main Authors:	Ross, Robert B., Amvrosiadis, George, Carns, Philip, Cranor, Charles D., Dorier, Matthieu, Harms, Kevin, Ganger, Greg, Gibson, Garth, Gutierrez, Samuel K., Latham, Robert, Robey, Bob, Robinson, Dana, Settlemyer, Bradley, Shipman, Galen, Snyder, Shane, Soumagne, Jerome, Zheng, Qing
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2020 Springer Springer Nature B.V Argonne National Laboratory, Lemont, IL 60439, U.S.A.%Parallel Data Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.%Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada%Los Alamos National Laboratory, Los Alamos NM, U.S.A.%The HDF Group, Champaign IL, U.S.A Springer Nature
Subjects:	Analysis Artificial Intelligence Computation Computational linguistics Computer Science Data Structures and Information Theory data-intensive computing distributed services High performance computing Information storage and retrieval Information Systems Applications (incl.Internet) Language processing MATHEMATICS AND COMPUTING Natural language interfaces New technology Performance evaluation Regular Paper Software Engineering storage and I/O Theory of Computation data-intensive computing storage and I/O high-performance computing distributed services
ISSN:	1000-9000, 1860-4749
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Abstract	Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. Rather than forcing all applications to use a one-size-fits-all data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. This paper introduces the Mochi framework and methodology. The Mochi core components and microservices are described. Examples of the application of the Mochi methodology to the development of four specialized services are detailed. Finally, a performance evaluation of a Mochi core component, a Mochi microservice, and a composed service providing an object model is performed. The paper concludes by positioning Mochi relative to related work in the HPC space and indicating directions for future work.
AbstractList	Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. Rather than forcing all applications to use a one-size- fitsall data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. This paper introduces the Mochi framework and methodology. The Mochi core components and microservices are described. Examples of the application of the Mochi methodology to the development of four specialized services are detailed. Finally, a performance evaluation of a Mochi core component, a Mochi microservice, and a composed service providing an object model is performed. The paper concludes by positioning Mochi relative to related work in the HPC space and indicating directions for future work. Keywords storage and I/O, data-intensive computing, distributed services, high-performance computing Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. Rather than forcing all applications to use a one-size-fits-all data staging and I/O software configuration, Mochi allows each application to use a data service specialized to its needs and access patterns. This paper introduces the Mochi framework and methodology. The Mochi core components and microservices are described. Examples of the application of the Mochi methodology to the development of four specialized services are detailed. Finally, a performance evaluation of a Mochi core component, a Mochi microservice, and a composed service providing an object model is performed. The paper concludes by positioning Mochi relative to related work in the HPC space and indicating directions for future work.
Audience	Academic
Author	Latham, Robert Settlemyer, Bradley Ross, Robert B. Carns, Philip Gibson, Garth Soumagne, Jerome Zheng, Qing Gutierrez, Samuel K. Shipman, Galen Amvrosiadis, George Robinson, Dana Snyder, Shane Dorier, Matthieu Harms, Kevin Cranor, Charles D. Robey, Bob Ganger, Greg
AuthorAffiliation	Argonne National Laboratory, Lemont, IL 60439, U.S.A.%Parallel Data Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.%Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada%Los Alamos National Laboratory, Los Alamos NM, U.S.A.%The HDF Group, Champaign IL, U.S.A
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Author_xml	– sequence: 1 givenname: Robert B. surname: Ross fullname: Ross, Robert B. email: rross@mcs.anl.gov organization: Argonne National Laboratory – sequence: 2 givenname: George surname: Amvrosiadis fullname: Amvrosiadis, George organization: Parallel Data Laboratory, Carnegie Mellon University – sequence: 3 givenname: Philip surname: Carns fullname: Carns, Philip organization: Argonne National Laboratory – sequence: 4 givenname: Charles D. surname: Cranor fullname: Cranor, Charles D. organization: Parallel Data Laboratory, Carnegie Mellon University – sequence: 5 givenname: Matthieu surname: Dorier fullname: Dorier, Matthieu organization: Argonne National Laboratory – sequence: 6 givenname: Kevin surname: Harms fullname: Harms, Kevin organization: Argonne National Laboratory – sequence: 7 givenname: Greg surname: Ganger fullname: Ganger, Greg organization: Parallel Data Laboratory, Carnegie Mellon University – sequence: 8 givenname: Garth surname: Gibson fullname: Gibson, Garth organization: Vector Institute for Artificial Intelligence – sequence: 9 givenname: Samuel K. surname: Gutierrez fullname: Gutierrez, Samuel K. organization: Los Alamos National Laboratory – sequence: 10 givenname: Robert surname: Latham fullname: Latham, Robert organization: Argonne National Laboratory – sequence: 11 givenname: Bob surname: Robey fullname: Robey, Bob organization: Los Alamos National Laboratory – sequence: 12 givenname: Dana surname: Robinson fullname: Robinson, Dana organization: The HDF Group – sequence: 13 givenname: Bradley surname: Settlemyer fullname: Settlemyer, Bradley organization: Los Alamos National Laboratory – sequence: 14 givenname: Galen surname: Shipman fullname: Shipman, Galen organization: Los Alamos National Laboratory – sequence: 15 givenname: Shane surname: Snyder fullname: Snyder, Shane organization: Argonne National Laboratory – sequence: 16 givenname: Jerome surname: Soumagne fullname: Soumagne, Jerome organization: The HDF Group – sequence: 17 givenname: Qing surname: Zheng fullname: Zheng, Qing organization: Parallel Data Laboratory, Carnegie Mellon University
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Copyright	Institute of Computing Technology, Chinese Academy of Sciences & Springer Nature Singapore Pte Ltd. 2020 COPYRIGHT 2020 Springer Institute of Computing Technology, Chinese Academy of Sciences & Springer Nature Singapore Pte Ltd. 2020. Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
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SubjectTerms	Analysis Artificial Intelligence Computation Computational linguistics Computer Science Data Structures and Information Theory data-intensive computing distributed services High performance computing Information storage and retrieval Information Systems Applications (incl.Internet) Language processing MATHEMATICS AND COMPUTING Natural language interfaces New technology Performance evaluation Regular Paper Software Engineering storage and I/O Theory of Computation
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