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

Thumbnail Image

Files

PopuriHPCF2017.pdf (2.58 MB)
matlabdata.dat (4.16 KB)
cg.r (1.68 KB)
driver_cg.r (1.95 KB)
driver_ge.r (1.65 KB)

Links to Files

https://userpages.umbc.edu/~gobbert/papers/PopuriHPCF2017.pdf

Permanent Link

http://hdl.handle.net/11603/11302

Collections

UMBC Mathematics and Statistics Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2017

Type of Work

Technical Report
Text

Department

Program

Citation of Original Publication

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.

Subjects

Matlab
Octave
Julia
R
high-performance numerical and statistical computing
UMBC High Performance Computing Facility (HPCF)
alternatives to Matlab for uses in research and teaching

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.