jPar - parallelizing Matlab calculations on multicores and in clusters without file communication

jPar is a simple parallelizing tool compatible with the old Paralize package, using Java mechanisms.
Updated 2 Dec 2015

View License

Jpar helps to make calculations based on embarrassingly parallel model (that is fork-join without communication and synchronization between instances) both within multicore machines and in clusters of computers. Unlike some other free parallel packages, where the communication and synchronization is realized by disk files (what involves busy waiting/active polling, is slow and sometimes causes errors), in jPar an external Java application is used instead.
The jPar package is almost as simple as Paralize from the user point of view (actually it is compatible with it), very easy to install, but it does not waste the cycles of cores on active polling and allows for using the machines with started Matlab instances to other purposes.
The package consists of only three elements:
1. Registration server
2. Solvers
3. Client.
The first process to be run is the registration server, whose task is to manage the set of solvers. The next step is to start Matlab instances and run one or more solvers from their sessions. It does not matter what operating systems are used. These Matlab sessions are blocked until the tasks are available. On the machine, where the server is running, one Matlab session must be the client session. It is started from Matlab console and divides input data to chunks along the third dimension of arrays, creates partial tasks and sends them via server to solvers. When the calculations are finished, the results are gathered in the client Matlab session.

The installation and use of jPar are described in README.TXT file.

The paper on jPar presented at FedCSIS 2015 conference is here:

Cite As

Andrzej Karbowski (2024). jPar - parallelizing Matlab calculations on multicores and in clusters without file communication (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Find more on Clusters and Clouds in Help Center and MATLAB Answers

Inspired by: paralize (v2006a)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes

A link to a conference paper was added.