@inproceedings{33ce0dc6859b44958f9c2fb46916ae03,
title = "Productive and Performant Generic Lossy Data Compression with LibPressio",
abstract = "In recent years, lossless and lossy compressors have been developed to cope with the ever increasing volume of scientific floating point data. However not all compression techniques are appropriate for all data-sets, and determining which one to use can be time consuming requiring code modifications and trial and error. We present LibPressio-a generic library for the compression of dense tensors that minimizes the code changes scientists need to make to take advantage of new and improved compression techniques. We compare LibPressio to 9 different competing libraries and measure the overhead of their design decisions as well as overall run time overhead showing insignificant overhead. We further show an improvement in usability as measured by a reduction in lines of code compared to native code by 50-90 %. The value of this tool can be seen by integration into Z-Checker and ADIOS2.",
keywords = "Error Bounded Lossy Compression, LibPressio",
author = "Robert Underwood and Victoriana Malvoso and Calhoun, {Jon C.} and Sheng Di and Franck Cappello",
note = "Funding Information: This research was funded grants from the National Science Foundation and the US Department of Energy Funding Information: This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations - the Office of Science and the National Nuclear Security Administration,responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation{\textquoteright}s exascale computing imperative. Funding Information: The material was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract DE-AC02-06CH11357. Funding Information: This material is based upon work supported by the National Science Foundation under grant numbers: NRT-DESE 1633608, OAC-2003709, and SHF-1910197, SHF-1617488, and CSSI-2104023/2104024. Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-7 2021 ; Conference date: 14-11-2021",
year = "2021",
doi = "10.1109/DRBSD754563.2021.00005",
language = "English (US)",
series = "Proceedings of DRBSD-7 2021: 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--10",
booktitle = "Proceedings of DRBSD-7 2021",
address = "United States",
}