Instructions

1. sudo -H pip install numpy

2. Git clone this repository

3. Change working directory to cloned repository

4. ./parallel_vs_serial_sort.py to pit my personal implementations of serial and parallel quicksort (of lists) against each other, and report the total time taken to run each of those two sorting algorithms/functions. A message of congratulations also appears for each of my personal implementations of quicksort if the implementation sorted a list correctly. Python's built-in "sorted" function is used to check the correctness of each implementation.

5. You may tweak the LENGTH_OF_STRING and LENGTH_OF_STRING_LIST global variables within the main "parallel_vs_serial_sort.py" file to see how each sorting algorithm/function operates under various different parameters. The ONLY stipulations on each of those global variables' value is that each of them must be a non-negative integer.

Sample Performance Metrics

• NOTE: I only picked the best results of multiple runs of the program for showing the maximum (and NOT the average) possible performance gain from parallelizing quicksort in the way that I have implemented it within "parallel_quicksort.py".

Main Program Output on Machine with Ryzen 5 2600

Sorting A List Of Length 2 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 2000000 strings where each string is 20 characters long...

...using serial version of quicksort: 10.430464 seconds.

...using parallel version of quicksort
    (parallelized over a target of 12 processes): 6.511897 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Sorting A List Of Length 10 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 10000000 strings where each string is 20 characters long...

...using serial version of quicksort: 62.619475 seconds.

...using parallel version of quicksort
    (parallelized over a target of 12 processes): 34.282575 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Main Program Output on Machine with Core i7 4800MQ

Sorting A List Of Length 2 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 2000000 strings where each string is 20 characters long...

...using serial version of quicksort: 10.929015 seconds.

...using parallel version of quicksort
    (parallelized over a target of 8 processes): 9.357903 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Sorting A List Of Length 10 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 10000000 strings where each string is 20 characters long...

...using serial version of quicksort: 66.533298 seconds.

...using parallel version of quicksort
    (parallelized over a target of 8 processes): 45.900663 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Main Program Output on Machine with Core i7 9700

Sorting A List Of Length 2 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 2000000 strings where each string is 20 characters long...

...using serial version of quicksort: 13.055551 seconds.

...using parallel version of quicksort
    (parallelized over a target of 8 processes): 11.536828 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Sorting A List Of Length 10 Million

Initializing list copies to be sorted (this may take some time)...

Generating reference sorted list using Python's "sorted" built-in function for
    validating correctness of serial version and parallel version of quicksort... Done!

Time to sort list of 10000000 strings where each string is 20 characters long...

...using serial version of quicksort: 78.760986 seconds.

...using parallel version of quicksort
    (parallelized over a target of 8 processes): 45.609169 seconds.

Validating result of serial version of quicksort...
    Congratulations, expected and actual lists are equal!

Validating result of parallel version of quicksort...
    Congratulations, expected and actual lists are equal!

Comments About Code in General

• Please refer to the file descriptions written at the top of each file within "serial_quicksort.py" and "parallel_quicksort.py" for where I got my implementations of serial and parallel quicksort from.

• I tried speeding up how fast the code ran first by compiling as much as possible into C using Cython. Unfortunately, the code failed to run faster after being compiled into C (and then compiled into an executable using gcc), as the compiled serial version of quicksort took 80+ seconds to run instead of 60+ seconds to run when sorting 10 million numbers. So I then tried using Numba's JIT compilation feature to speed up the code, which also failed to make the code run faster (i.e. JIT compiled serial quicksort took 80+ seconds to run again instead of 60+ seconds). Numba also failed to JIT compile the parallel version of quicksort because Numba didn't know how to compile the types such as multiprocessing.connection.Connection under the multiprocessing module. Therefore, as far as I'm aware, I have no choice but to leave the code as it is in native Python for demonstrating serial and parallel quicksort performance.

Comments About Program Performance In General

• For some reason that I've not been able to completely figure out yet, there is sometimes up to a ~60%+ discrepancy in how fast the parallel quicksort portion of the program runs from each run to each run of the main program. This is also without changing ANYTHING about how my machine runs or changing anything about how the overall program runs. I suspect that it has something to do with inter- process communication overhead, as I've noticed in my 'Task Manager' how processes are spawned quickly when the parallel quicksort function executes, but it takes a while for each spawned process to transition from the zombie state (where each spawned process's parent process is presumably finishing receiving the sorted partitions of the array) to being terminated. Unfortunately, due to my general lack of knowledge (as of right now) on how inter-process communication is implemented with the Connection class under Python3's multiprocessing module, I cannot confirm or deny my current speculations about the performance discrepancies.