Faster Parallel Processing Alternatives to Multithreading in Python

Mar 17, 2024 ยท 2 min read

Multithreading in Python allows concurrent execution of multiple threads within a process. This enables parallelism and improved performance for I/O-bound tasks. However, multithreading has some limitations:

  • The Python GIL (Global Interpreter Lock) prevents true parallel execution on a single interpreter process. Only one thread can execute Python bytecodes at a time.
  • There is overhead in context switching between threads.
  • Shared memory and synchronization primitives like locks can make code complicated.
  • So when do we need alternatives? CPU-bound numeric processing is where Python's multithreading falls short. The GIL causes only one CPU to be utilized even with multiple threads.

    Some better options:


    The multiprocessing module spawns multiple Python interpreter processes to achieve parallelism. Each process runs independently without the GIL limitation. Multiprocessing avoids shared memory, using message passing for coordination.

    from multiprocessing import Pool
    def cube(x):
        return x*x*x
    if __name__ == '__main__':
        with Pool() as pool:
            results =, range(8)) 

    While multiprocessing avoids the GIL, there is still interprocess communication overhead. And multiprocessing won't help for parallelism in a single Python function call.


    Numba gives native machine code compilation for NumPy array operations. Just decorate your function with @numba.jit and Numba will compile it to optimized machine code using LLVM. This avoids the GIL by running outside the Python interpreter, giving parallel speedups on multicore CPUs or GPUs.


    For more versatility than Numba, Cython compiles Python-like code down to C extensions. These C extensions interface back to Python but avoid interpreted overhead and the GIL during computation. Cython enables calling multi-threaded C libraries for further parallel gains.

    So in summary, for more scalable parallelism, look beyond Python threads to multiprocessing, Numba GPU/CPU vectorization, or Cython C extensions. But also consider higher level libraries like Dask for parallel analytics. The key is fitting the solution to your specific performance and complexity requirements.

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