MathWorks extends parallel programming language

MASSACHUSETTS, USA: The MathWorks has announced that its Parallel Computing Toolbox now enables MATLAB users to create parallel applications and distribute them to other users as standalone executables or software components for use on computer clusters. In addition, Parallel Computing Toolbox introduces a new language construct, called spmd, which simplifies the development of data-intensive parallel applications.

By integrating Parallel Computing Toolbox with MATLAB Compiler, we are enabling seamless usage of parallel applications from research to production. Professionals throughout an organization can now leverage MATLAB in their day-to-day work.

With this new release, MATLAB users can convert parallel MATLAB applications into executables or shared libraries and provide them to their own end-users royalty-free. This is possible by running applications developed with Parallel Computing Toolbox through MATLAB Compiler. The resulting executables and libraries can take advantage of additional computational power offered by MATLAB Distributed Computing Server running on a computer cluster. As a result, a broad class of professionals who do not work with MATLAB directly, are able to benefit from parallel MATLAB capabilities.

News Image “At The MathWorks, we are committed to addressing the needs of different groups of users across organizations,” said Silvina Grad-Freilich, manager of parallel computing and application deployment marketing at The MathWorks. “By integrating Parallel Computing Toolbox with MATLAB Compiler, we are enabling seamless usage of parallel applications from research to production. Professionals throughout an organization can now leverage MATLAB in their day-to-day work.”

As part of the upgrade to Parallel Computing Toolbox, The MathWorks added new features to the parallel MATLAB language to simplify the development of applications that deal with very large data sets. Users can now annotate sections of their MATLAB code with the new spmd language feature, enabling the parallel execution of the code on large data that is distributed across separate cores or processors. All necessary commands and data are automatically transferred to MATLAB sessions running on these cores, without the need of user intervention. With language features such as spmd, users solve large computationally- and data-intensive technical problems by making minimal to no code changes to their existing code. These features enable engineers and scientists to focus on solving their problems without having to learn a low-level parallel language or worry about the underlying hardware or network configuration.