A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya

Popuri, Sai K.; Gobbert, Matthias K.

A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya

dc.contributor.author	Popuri, Sai K.
dc.contributor.author	Gobbert, Matthias K.
dc.date.accessioned	2018-09-13T20:07:28Z
dc.date.available	2018-09-13T20:07:28Z
dc.date.issued	2017
dc.description.abstract	Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated compiler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) processors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. The syntax of Julia is closer to that of Matlab than it is to R. The tests focused on efficiency show that while Matlab, Octave, and Julia were fundamentally able to solve problems of the same size, Matlab and Julia were found to be closer in terms of efficiency in absolute run times, especially for large sized problems.	en_US
dc.description.sponsorship	The first author acknowledges financial support from the UMBC High Performance Computing Facility (HPCF). The hardware used in the computational studies is part of HPCF, which is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258 and CNS–1228778) and the SCREMS program (grant no. DMS– 0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources	en_US
dc.description.uri	https://userpages.umbc.edu/~gobbert/papers/PopuriHPCF2017.pdf	en_US
dc.format.extent	37 pages	en_US
dc.genre	Technical Report	en_US
dc.identifier	doi:10.13016/M2Q23R39S
dc.identifier.uri	http://hdl.handle.net/11603/11302
dc.language.iso	en_US	en_US
dc.relation.isAvailableAt	The University of Maryland, Baltimore County (UMBC)
dc.relation.ispartof	UMBC Mathematics Department Collection
dc.relation.ispartof	UMBC Faculty Collection
dc.relation.ispartofseries	HPCF Technical Report HPCF-2017-03
dc.rights	This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subject	Matlab	en_US
dc.subject	Octave	en_US
dc.subject	Julia	en_US
dc.subject	R	en_US
dc.subject	high-performance numerical and statistical computing	en_US
dc.subject	UMBC High Performance Computing Facility (HPCF)	en_US
dc.subject	alternatives to Matlab for uses in research and teaching
dc.title	A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya	en_US
dc.type	Text	en_US