The number of workers being used in MATLAB can significantly affect your simulation time, especially in parallel computing tasks. Using more or fewer workers than the number of physical cores can impact performance. Each worker adds memory overhead, which can become a bottleneck based on the memory usage of individual tasks.
For instance, the "parfor" function spawns one MATLAB process per worker in the cluster in "no-display" mode. Although the startup time of these processes adds to the execution time, it only occurs once.
Typically, Operating system assigns each worker to a CPU core. However, if a core is heavily loaded, two workers might compete for scheduling on a single core, leading to context switches between worker processes. While context switching is not inherently detrimental to execution time, problems arise when the time spent switching is significant compared to the actual process execution time.
Therefore, a general rule is to set the number of workers equal to the number of physical cores. Using fewer workers underutilizes the CPU, while using more may increase context switches, depending on the CPU load.
It is possible that for certain tasks and CPU conditions, assigning more workers than physical cores could enhance performance. Ultimately, it depends on the specific task at hand.
I have developed a MATLAB script (attached) that performs a computationally intensive, parallelizable task. The script measures execution time for varying numbers of workers and plots the results to visualize how execution time changes with respect to the number of workers. Below image shows one run of the attached MATLAB script, the blue circle is the point that shows the lowest execution time.
In my tests, the optimal number of workers for this task was between 10-15, which exceeds my physical core count of 10. This suggests that, depending on the task, using more workers than physical cores can enhance performance.
For more details on recommended number of workers in MATLAB, you can refer to this answer too:
