Create distributed quick sort algorithm

[zhe-thoughts]

This algorithm extends from quick_sort.py in this same directory. It uses Ray to make quick sort execute in distributed manner.

On my local machine, it has 3x speedup:

Sequential execution: 16.010289192199707
Distributed execution: 5.844448089599609

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
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[zhe-thoughts]

NOTE: this algorithm extends from the existing quick_sort.py in the same directory. Please let me know how we usually handles this (should I import it? seems an overkill since the code is very simple)

[TravisBuddy]

Travis tests have failed

Hey @zhe-thoughts,
Please read the following log in order to understand the failure reason.
It'll be awesome if you fix what's wrong and commit the changes.

TravisBuddy Request Identifier: 54c59f00-09ff-11eb-b8c5-47e0b209ea74

[seelam]

I gave this a try... all looks good but the WARNINGS are annoying and it will be good to limit the concurrency via an API or config call:

ubuntu@seelam-ansible-host:~$ python quicksort.py
....
Sequential execution: 20.511595964431763
2020-10-18 22:02:01,225 WARNING worker.py:1047 -- WARNING: 24 PYTHON workers have been started. This could be a result of using a large number of actors, or it could be a consequence of using nested tasks (see ray-project/ray#3644) for some a discussion of workarounds.
....
Distributed execution: 10.588677167892456

In your case, since you are using the regular quick sort for <200K elements and total elements are 1M, only ~10 actors. When I changed the 200k to 20k, the distributed version took 4x longer than sequential version:
Distributed execution: 84.4353358745575

I guess it is important to figure out at what granularity of computation, you need to use the distributed version.

[stale]

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[stale]

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