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

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Brahim - 2024-11-27T21:03:24+00:00
Question: 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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    • Expert Answer

    Profile picture of Kshitij Singh Kshitij Singh answered . 2025-11-20

    MATLAB relies on the underlying operating system and hardware-level information to determine and utilize CPU resources, including Performance cores (P-cores) and Efficiency cores (E-cores) in modern CPUs. Here's how MATLAB handles this situation and what distinctions it makes:

    1. Role of the Operating System

    • MATLAB queries the operating system for the number of available CPU cores when determining the maximum number of computational threads. The OS provides a logical abstraction of the hardware, which may or may not explicitly differentiate between P-cores and E-cores.
    • For example, in Windows, Linux, and macOS, the OS scheduler determines how tasks are distributed across P-cores and E-cores based on workload characteristics and hardware support like Intel's Thread Director.

    2. MATLAB's Use of Cores

    • MATLAB’s parallel computing and multithreading features, such as those used by parfor, parfeval, and matrix operations in BLAS (Basic Linear Algebra Subprograms), depend on available computational threads.
    • When you call maxNumCompThreads, MATLAB retrieves the maximum number of threads the OS allows for MATLAB's computation. However, it does not explicitly distinguish between P-cores and E-cores.

    3. Hardware Awareness

    • Modern processors (e.g., Intel Alder Lake and later) feature Thread Director technology, which works with the OS to assign threads to appropriate cores based on task requirements. MATLAB indirectly benefits from this capability:
      • Computationally intensive tasks (e.g., linear algebra, FFT) are more likely to be executed on P-cores.
      • Lighter tasks (e.g., background tasks, I/O) may be offloaded to E-cores by the OS scheduler.
    • However, MATLAB does not have built-in functions to explicitly identify or control whether a specific task uses a P-core or an E-core.

    4. Parallel Computing Toolbox

    • With the Parallel Computing Toolbox, MATLAB allows you to create and manage a pool of workers. These workers can run as threads or processes, depending on your configuration.
    • While you cannot directly control whether a worker runs on a P-core or E-core, the OS and hardware-level optimizations typically ensure efficient core usage.
    • To check how many workers are available, you can use:

     

    pool = parpool; % Start a parallel pool
numWorkers = pool.NumWorkers; % Number of parallel workers

    5. Explicit Core Control (Not MATLAB's Native Feature)

    • If you need to explicitly target specific cores, you would need to use platform-specific tools:

      • On Windows, you can use Processor Affinity via tools like Task Manager or APIs.
      • On Linux, you can use taskset to bind processes to specific cores.

    • However, this is outside MATLAB's built-in capabilities and requires external configuration.

    Summary

    • MATLAB does not explicitly distinguish between P-cores and E-cores.
    • The distinction and utilization of P-cores vs. E-cores are managed by the operating system and hardware technologies like Intel Thread Director.
    • MATLAB focuses on utilizing the maximum number of computational threads, and the OS ensures that the appropriate cores are used for each task.
    • For explicit core-level control, you would need to use OS-level tools.


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