Matlab can use multithreading and parallel computing to speed up computations , which may have an impact on performance in shared workspaces.
Matlab is resource-greedy by default when computing built-in functions with implicit multithreading, of which there are many
multithreading resources in Matlab can be managed with the
-singleCompThreadstartup option or the
maxNumCompThreadsfunction to limit the number of computational threads available
additionally, parallel computing resources in Matlab can be managed using the
parpoolfunction to limit the number of parallel 'workers' used by both built-in functions with implicit parallelisation and custom functions that use defined parallel programming constructs
A number of functions are implicitly multithreaded by default  in Matlab releases since 2008a .
This has been identified as a potential issue since Matlab will use all available processors for implicitly multithreaded functions .
The Matlab function
maxNumCompThreads  can be used to both set and query the current maximum number of computational threads, though this function is being deprecated.
maxNumCompThreads returned 32 when recently checked.
As a workaround, Matlab can be started with the command line option
-singleCompThread , which is suggested by various research computing guides   , found via a quick online search of the topic.
Resource management on beastiexx may be improved by:
calling Matlab with the
-singleCompThreadstartup option, or
reducing the computational threads available to Matlab using the
maxNumCompThreadsfunction, ideally as part of
startup.m, until it is removed in a future version.
Matlab can perform parallel computations  using built-in functions with implicit parallelisation  or using defined programming constructs .
Resources allocated for parallel computing can be defined using the
parpool function .
Sharing of resources may be fascilitated through use of the Distributed Computing Server , though usability may be worth review prior to implementation .
Run MATLAB on multicore and multiprocessor machines
Which MATLAB functions benefit from multithreaded computation?
MATLAB overutilizing CPUS
Matlab: Control maximum number of computational threads
Commonly Used Startup Options: singleCompThread
How to run MATLAB jobs on the TIGRESS clusters
High Throughput Computing: How To Run Matlab
Parallel Computing Toolbox
Built-in Parallel Computing Support in MathWorks Products
Matlab Programming Parallel Application
Matlab: Create parallel pool on cluster
Matlab: Distributed Computing Server
A review of Matlab Distributed Computing Server