LibPressio-Predict: Flexible and Fast Infrastructure For Inferring Compression Performance-Reference-Cited by-同舟云学术

LibPressio-Predict: Flexible and Fast Infrastructure For Inferring Compression Performance

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
language:
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Author:

Underwood Robert R.¹^ORCID,Di Sheng²^ORCID,Jin Sian³^ORCID,Rahman Md Hasanur⁴^ORCID,Khan Arham⁵^ORCID,Cappello Franck²^ORCID

Affiliation:

1. Mathematics and Computer Science Division, Argonne National Laboratory (ANL), United States of America and University of Chicago, United States of America

2. Mathematics and Computer Science Division, Argonne National Laboratory, USA and University of Chicago, USA

3. Indiana University, United States of America

4. University of Iowa, United States of America

5. University of Chicago, United States of America

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3625124

Reference23 articles.

1. 2021. MPI: A Message-Passing Interface Standard Version 4. https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf 2021. MPI: A Message-Passing Interface Standard Version 4. https://www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf

2. Ganguli Arkaprabha , robert underwood , Julie Bessac, David Krasowska, Jon C. Calhoun, Sheng Di, and Franck Cappello. 2023 . A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression. IEEE, Sante Fe, New Mexico . Ganguli Arkaprabha, robert underwood, Julie Bessac, David Krasowska, Jon C. Calhoun, Sheng Di, and Franck Cappello. 2023. A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression. IEEE, Sante Fe, New Mexico.

3. Use cases of lossy compression for floating-point data in scientific data sets

4. Clairvoyant prefetching for distributed machine learning I/O

5. Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling