[DO NOT MERGE] Some draft code for dataset preprocess optimization

[caojy1998]

Description

This code is a draft for the preprocess algorithm. Here we deal with the first outer sort algorithm and leaving the construction of csc graph for future exploration.
The function _graph_data_to_fused_csc_sampling_graph consists 2 part.

1. load in the coo format graph stored as csv format ~20 s
2. convert the coo format to csv format ~ 2 s
   In our approach:
3. read in the csv file part by part and convert it to numpy format and store it back to the disk.
4. Read in the numpy file and perform a merge sort to convert the coo to a sorted coo. (This part should be further optimized since currently we read in the whole numpy file. However, we should read in them part by part to same the memory consumption)
5. Store the sorted coo part by part to the disk for further reading
6. read the sorted coo and convert it to csc using a for loop.

The first 3 step has been optimized. While the fourth one is not.

Checklist

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
• I've leverage the tools to beautify the python and c++ code.
• The PR is complete and small, read the Google eng practice (CL equals to PR) to understand more about small PR. In DGL, we consider PRs with less than 200 lines of core code change are small (example, test and documentation could be exempted).
• All changes have test coverage
• Code is well-documented
• To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
• Related issue is referred in this PR
• If the PR is for a new model/paper, I've updated the example index here.

Changes

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[Rhett-Ying]

@caojy1998 any detailed performance number for the current improvement?

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Commit ID: b6884e401e826b443f3333f296404b35c350b768

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