Browsing by Subject "Octave"
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Item A Comparative Evaluation of Matlab, Octave, FreeMat, and Scilab on Tara(2011) Brewster, Matthew W.; Gobbert, Matthias K.Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave, FreeMat, and Scilab are free numerical computational packages that have many of the same features as Matlab. They are available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether these packages are viable alternatives to Matlab for uses in research and teaching. We compare the results on the cluster tara in the UMBC High Performance Computing Facility with 86 nodes, each with two quadcore Intel Nehalem processors and 24 GB of memory. The tests focused on usability lead us to conclude that the package Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. Both FreeMat and Scilab were hampered by somewhat different syntax or function names and some missing functions. The tests focused on efficiency show that Matlab and Octave are fundamentally able to solve problems of the same size and with equivalent efficiency in absolute times, except in one test dealing with a very large problem. FreeMat and also Scilab exhibit significant limitations on the problem size and the efficiency of the problems they can solve in our tests. In summary, we conclude that Octave is the best viable alternative to Matlab because it was not only fully compatible with Matlab in our tests, but it also performed very well.Item A Comparative Evaluation of Matlab, Octave, FreeMat, Scilab, and R on Tara(2012) Popuri, Sai K.; Raim, Andrew M.; Brewster, Matthew W.; Gobbert, Matthias K.Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave, FreeMat and Scilab are free numerical computational packages that have many of the same features as Matlab. R is a free Statistical package. Although R does not belong to the same line of products as Matlab, it is similar to Matlab in its computational capabilities. These packages are available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether they are viable alternatives to Matlab for uses in research and teaching. We compare the results on the cluster tara in the UMBC High Performance Computing Facility with 86 nodes, each with two quadcore Intel Nehalem processors and 24 GB of memory. The tests focused on usability lead us to conclude that the package Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. Both FreeMat and Scilab were hampered by somewhat different syntax or function names and some missing functions. The tests focused on efficiency show that Matlab and Octave are fundamentally able to solve problems of the same size and with equivalent efficiency in absolute times, except in one test dealing with a very large problem. FreeMat and also Scilab exhibit significant limitations on the problem size and the efficiency of the problems they can solve in our tests. The syntax of R is significantly different from that of Matlab, Octave, FreeMat, and Scilab. R too exhibited certain limitations on the size of problems it could solve for and its performance was similar to that of FreeMat and Scilab. In summary, we conclude that Octave is the best viable alternative to Matlab because it was not only fully compatible (in terms of syntax) with Matlab in our tests, but it also performed very well.Item A Comparative Evaluation of Matlab, Octave, FreeMat, Scilab, R, and IDL on Tara(2012) Coman, Ecaterina; Brewster, Matthew W.; Popuri, Sai K.; Raim, Andrew M.; Gobbert, Matthias K.Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. IDL, a commercial package used for data analysis, along with the free numerical computational packages Octave, FreeMat, Scilab, and the statistical package R shares many of the same features as Matlab. They are available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether these packages are viable alternatives to Matlab for uses in research and teaching. We compare the results on the cluster tara in the UMBC High Performance Computing Facility with 86 nodes, each with two quadcore Intel Nehalem processors and 24 GB of memory. The tests focused on usability lead us to conclude that the package Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. FreeMat, Scilab, R, and IDL were hampered by somewhat different syntax or function names and some missing functions. The tests focused on efficiency show that Matlab and Octave are fundamentally able to solve problems of the same size and with equivalent efficiency in absolute times, except in one test dealing with a very large problem. Also IDL performed equivalently in the case of iterative methods. FreeMat, Scilab, and R exhibit significant limitations on the problem size and the efficiency of the problems they can solve in our tests. The syntax of R and IDL are significantly different from that of Matlab, Octave, FreeMat, and Scilab. In summary, we conclude that Octave is the best viable alternative to Matlab because it was not only fully compatible (in terms of syntax) with Matlab in our tests, but it also performed very well.Item A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya(2017) Popuri, Sai K.; Gobbert, Matthias K.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.Item A Comparison of Solving the Poisson Equation Using Several Numerical Methods in Matlab and Octave on the Cluster maya(2014) Swatski, Sarah; Khuvis, Samuel; Gobbert, Matthias K.Systems of linear equations resulting from partial differential equations arise frequently in many phenomena such as heat, sound, and fluid flow. We apply the finite difference method to the Poisson equation with homogeneous Dirichlet boundary conditions. This yields in a system of linear equations with a large sparse system matrix that is a classical test problem for comparing direct and iterative linear solvers. We compare the performance of Gaussian elimination, three classical iterative methods, and the conjugate gradient method in both Matlab and Octave. Although Gaussian elimination is fastest and can solve large problems, it eventually runs out of memory. If very large problems need to be solved, the conjugate gradient method is available, but preconditioning is vital to keep run times reasonable. Both Matlab and Octave perform well with intermediate mesh resolutions; however, Matlab is eventually able to solve larger problems than Octave and runs moderately faster.