Scalable computing in Java with PCJ Library. Improved collective operations

Nowicki, Marek; Górski, Łukasz; Bała, Piotr

Scalable computing in Java with PCJ Library. Improved collective operations

dc.contributor.author	Nowicki, Marek
dc.contributor.author	Górski, Łukasz
dc.contributor.author	Bała, Piotr
dc.contributor.organization	Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń	en
dc.contributor.organization	Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw	en
dc.date.accessioned	2022-07-29T16:44:23Z
dc.date.available	2022-07-29T16:44:23Z
dc.date.issued	2021
dc.description.abstract	Machine learning and Big Data workloads are becoming as important as traditional HPC ones. AI and Big Data users tend to use new programming languages such as Python, Julia, or Java, while the HPC community is still dominated by C/C++ or Fortran. Hence, there is a need for new programming libraries and languages that will integrate different applications and allow them to run on large computer infrastructure. Since modest computers are multinode and multicore, parallel execution is an additional challenge here. For that purpose, we have developed the PCJ library, which introduces parallel programming capabilities to Java using the Partitioned Global Address Space model. It does not modify language nor running environment (JVM). The PCJ library allows for easy development of parallel code and runs it on laptops, workstations, supercomputers, and the cloud. This paper presents an overview of the PCJ library and its usage in parallelizing selected workloads, including HPC, AI, and Big Data. The performance and scalability are presented. We present recent addition to the PCJ library, which are collective operations. The collective operations significantly reduce the number of lines of code to write, ensuring good performance	en
dc.identifier.citation	M. Nowicki, Ł. Górski and P. Bała. Scalable computing in Java with PCJ Library. Improved collective operations. In: Proceedings of International Symposium on Grids and Clouds 2021 - Proceedings of Science(ISGC2021), 2021, vol. 378, p. 7; DOI: https://doi.org/10.22323/1.378.0007	en
dc.identifier.doi	10.22323/1.378.0007
dc.identifier.issn	1824-8039
dc.identifier.uri	https://open.icm.edu.pl/handle/123456789/21550
dc.language.iso	en
dc.publisher	Sissa Medialab srl	en
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Java	en
dc.subject	parallel computing	en
dc.subject	big data	en
dc.subject	machine learning	en
dc.subject	partitioned global address space	en
dc.title	Scalable computing in Java with PCJ Library. Improved collective operations	en
dc.type	article	en