The Adaptable IO System (ADIOS)

David Pugmire; Norbert Podhorszki; Scott Klasky; Matthew Wolf; James Kress; Mark Kim; Nicholas Thompson; Jeremy Logan; Ruonan Wang; Kshitij Mehta; Eric Suchyta; William Godoy; Jong Choi; George Ostrouchov; Lipeng Wan; Jieyang Chen; Berk Geveci Chuck Atkins; Caitlin Ross; Greg Eisenhauer; Junmin Gu; John Wu; Axel Huebl; Seiji Tsutsumi

doi:10.1007/978-3-030-81627-8_11

The Adaptable IO System (ADIOS)

David Pugmire, Norbert Podhorszki, Scott Klasky, Matthew Wolf, James Kress, Mark Kim, Nicholas Thompson, Jeremy Logan, Ruonan Wang, Kshitij Mehta, Eric Suchyta, William Godoy, Jong Choi, George Ostrouchov, Lipeng Wan, Jieyang Chen, Berk Geveci Chuck Atkins, Caitlin Ross, Greg Eisenhauer, Junmin GuJohn Wu, Axel Huebl, Seiji Tsutsumi

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

The Adaptable I/O System (ADIOS) provides a publish/subscribe abstraction for data access and storage. The framework provides various engines for producing and consuming data through different mediums (storage, memory, network) for various application scenarios. ADIOS engines exist to write/read files on a storage system, to couple independent simulations together or to stream data from a simulation to analysis and visualization tools via the computer’s network infrastructure, and to stream experimental/observational data from the producer to data processors via the wide-area-network. Both lossy and lossless compression are supported by ADIOS to provide for seamless exchange of data between producer and consumer. In this work we provide a description for the ADIOS framework and the abstractions provided. We demonstrate the capabilities of the ADIOS framework using a number of examples, including strong coupling of simulation codes, in situ visualization running on a separate computing cluster, and streaming of experimental data between Asia and the United States.

Original language	English
Title of host publication	Mathematics and Visualization
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	233-254
Number of pages	22
DOIs	https://doi.org/10.1007/978-3-030-81627-8_11
State	Published - 2022

Publication series

Name	Mathematics and Visualization
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Funding

Acknowledgements This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

Funders	Funder number
Office of Science
National Nuclear Security Administration

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10.1007/978-3-030-81627-8_11

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Pugmire, D., Podhorszki, N., Klasky, S., Wolf, M., Kress, J., Kim, M., Thompson, N., Logan, J., Wang, R., Mehta, K., Suchyta, E., Godoy, W., Choi, J., Ostrouchov, G., Wan, L., Chen, J., Atkins, B. G. C., Ross, C., Eisenhauer, G., ... Tsutsumi, S. (2022). The Adaptable IO System (ADIOS). In Mathematics and Visualization (pp. 233-254). (Mathematics and Visualization). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-81627-8_11

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abstract = "The Adaptable I/O System (ADIOS) provides a publish/subscribe abstraction for data access and storage. The framework provides various engines for producing and consuming data through different mediums (storage, memory, network) for various application scenarios. ADIOS engines exist to write/read files on a storage system, to couple independent simulations together or to stream data from a simulation to analysis and visualization tools via the computer{\textquoteright}s network infrastructure, and to stream experimental/observational data from the producer to data processors via the wide-area-network. Both lossy and lossless compression are supported by ADIOS to provide for seamless exchange of data between producer and consumer. In this work we provide a description for the ADIOS framework and the abstractions provided. We demonstrate the capabilities of the ADIOS framework using a number of examples, including strong coupling of simulation codes, in situ visualization running on a separate computing cluster, and streaming of experimental data between Asia and the United States.",

author = "David Pugmire and Norbert Podhorszki and Scott Klasky and Matthew Wolf and James Kress and Mark Kim and Nicholas Thompson and Jeremy Logan and Ruonan Wang and Kshitij Mehta and Eric Suchyta and William Godoy and Jong Choi and George Ostrouchov and Lipeng Wan and Jieyang Chen and Atkins, {Berk Geveci Chuck} and Caitlin Ross and Greg Eisenhauer and Junmin Gu and John Wu and Axel Huebl and Seiji Tsutsumi",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

year = "2022",

doi = "10.1007/978-3-030-81627-8_11",

language = "English",

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Pugmire, D , Podhorszki, N , Klasky, S, Wolf, M, Kress, J, Kim, M, Thompson, N, Logan, J, Wang, R, Mehta, K , Suchyta, E , Godoy, W , Choi, J, Ostrouchov, G, Wan, L, Chen, J, Atkins, BGC, Ross, C, Eisenhauer, G, Gu, J, Wu, J, Huebl, A & Tsutsumi, S 2022, The Adaptable IO System (ADIOS). in Mathematics and Visualization. Mathematics and Visualization, Springer Science and Business Media Deutschland GmbH, pp. 233-254. https://doi.org/10.1007/978-3-030-81627-8_11

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AU - Kim, Mark

AU - Thompson, Nicholas

AU - Logan, Jeremy

AU - Wang, Ruonan

AU - Mehta, Kshitij

AU - Suchyta, Eric

AU - Godoy, William

AU - Choi, Jong

AU - Ostrouchov, George

AU - Wan, Lipeng

AU - Chen, Jieyang

AU - Atkins, Berk Geveci Chuck

AU - Ross, Caitlin

AU - Eisenhauer, Greg

AU - Gu, Junmin

AU - Wu, John

AU - Huebl, Axel

AU - Tsutsumi, Seiji

PY - 2022

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The Adaptable IO System (ADIOS)

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