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A practical comparision of multitasking libraries

The following code-base attempts to compare multiple libraries on a simple experiment of Algebraic operations.

The results are in favour of certain libraries because they are more natural in he application of Compute bound tasks, whereas the others are better at I/O bound tasks. Threading in Python is considered broken... I've also refrained from using the MPI library of C.

I've made a driver.py program which create 5 sets of test-cases of increasing size to test the excution times of each version of each program.

The comparison is across different libraries and languages, with the following programs:

C:
  • optimal serial code
  • openMP directive based parallelism
  • pthread library
Python:
  • optimal serial
  • pyMP
  • Python multiprocessing module
Note that the gcc compiler automatically vectorizes the addition of arrays x and y using SIMD compliant hardware and appropriate data-types.

I'll be adding the GPU comparison as soon as I can! Currently I don't have a GPGPU compatible graphics card on my device...


Open source Code:

https://github.com/varun-manjunath/ParallelComputing/tree/master/library_comparison
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