How MATLAB makes the distinction between P-Cores and E-Cores?

 It is known that modern CPUs have both Performance cores (P-cores) and efficiency cores (E-cores), different types of CPU cores that have different purposes and are designed for different tasks. P-cores typically have higher clock speeds and designed for high-performance tasks, while E-cores operate at lower clock speeds and focus on energy-efficient processing.

In MATLAB, maxNumCompThreads returns the current maximum number of computational threads. Currently, the maximum number of computational threads is equal to the number of physical cores on your machine.

How MATLAB makes the distinction between P-Cores and E-Cores ?

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If the question is about "how MATLAB maximize it's performance with the knowledge of a hybrid structured CPU", then part 1 helps with it:

Parallel Computing Toolbox can use both types of cores. MATLAB will rely on the operating system to schedule processes across the available cores, whether they are performance-oriented or designed for efficiency. However, it's important to note that while PCT can utilize both performance (P) and efficiency (E) cores, the best practice for computational tasks is to start with a number of parallel workers equivalent to the number of physical P cores. This is because P cores are akin to traditional physical cores in terms of computational capability, while E cores are optimized for power-saving and less intensive tasks.

From R2024a onwards Parallel Computing Toolbox's local 'Processes' profile will use a number of default workers equal to the number of P cores on the machine. This can be altered by the same steps in the link at the bottom.

Windows Power Plan

If you are using Windows, you may wish to look at adjusting your Windows Power Plan. If this is set to ‘balanced’ then Windows may deprioritise background or unselected applications so they will only run on the E cores of the system. Setting the power plan to ‘High Performance’ should cause the Intel Thread Director to be able to select which workloads are best suited to each core.

Performance Expectations

You may still see performance gains when including efficiency cores in your parallel computations, but the scaling may differ from performance cores. The actual performance improvement will depend on the nature of your code and the specific tasks being executed. In some cases, efficiency cores can handle less demanding parallel tasks effectively, allowing the performance cores to focus on more intensive computations.

Part 2. If the question is really about "How MATLAB estimate the exact number of P-cores and E-cores on a given system" Then it is very hard to tell because MATLAB is a closed source software package that targets multiple OS and hardware platforms, and they constantly make changes under the hood without notifications.

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