Accelerating Communications in Federated Applications with Transparent Object Proxies

Author:

Pauloski J. Gregory1ORCID,Hayot-Sasson Valerie1ORCID,Ward Logan2ORCID,Hudson Nathaniel1ORCID,Sabino Charlie1ORCID,Baughman Matt1ORCID,Chard Kyle13ORCID,Foster Ian12ORCID

Affiliation:

1. University of Chicago, Chicago, United States of America

2. Argonne National Laboratory, Chicago, United States of America

3. Argonne National Laboratory (ANL), Chicago, United States of America

Funder

NSF (National Science Foundation)

DOE U.S. Department of Energy

Publisher

ACM

Reference63 articles.

1. Martín Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , : A system for large-scale machine learning . In 12th USENIX Symposium on Operating Systems Design and Implementation , pages 265 -- 283 . USENIX Association , 2016 . Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, pages 265--283. USENIX Association, 2016.

2. Linda and Friends

3. aiortc. https://github.com/aiortc/aiortc. Accessed Mar 2023 . aiortc. https://github.com/aiortc/aiortc. Accessed Mar 2023.

4. WA-Dataspaces: Exploring the Data Staging Abstractions for Wide-Area Distributed Scientific Workflows

5. ExaWorks: Workflows for Exascale

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