![]() Simultaneous multithreading gives the appearance that a computer has twice as many cores than it actually has. Virtual cores may modestly improve overall system performance, but they are likely to have little effect on the performance of MATLAB applications. However, on some processors, a single FPU may be shared between multiple CPU cores, potentially creating a performance bottleneck. On many CPUs, the number of Floating-Point Units (FPUs) equals the number of CPU cores. MATLAB performance is dependent on the presence of floating-point hardware. For additional capability, Parallel Computing Toolbox offers parallel programming constructs that more directly leverage multiple computer cores. But not all MATLAB functions are multithreaded, and the speed-up varies with the algorithm. ![]() MATLAB automatically uses multithreading to exploit the natural parallelism found in many MATLAB applications. ![]() Computers with more CPU cores can outperform those with a lower core count, but results will vary with the MATLAB application.
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