Faster Parallel Processing Alternatives to Multithreading in Python

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:

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:

Multiprocessing

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 = pool.map(cube, range(8)) 
        print(results)

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

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.

Cython

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.