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[FEA]: MG Testing should use datasets that requier more than 1 GPU #3282

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BradReesWork opened this issue Feb 14, 2023 · 1 comment · Fixed by #3540
Closed
2 tasks done

[FEA]: MG Testing should use datasets that requier more than 1 GPU #3282

BradReesWork opened this issue Feb 14, 2023 · 1 comment · Fixed by #3540
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@rlratzel
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@BradReesWork
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Is this a new feature, an improvement, or a change to existing functionality?

Improvement

How would you describe the priority of this feature request

Critical (currently preventing usage)

Please provide a clear description of problem this feature solves

The current set of MG test use a dataset that is too small, it is even small for SG testing.
The datasets need to be over 200 million edges, 1 billion edges and larger preferred

Nothing smaller than the cyber.csv , Twitter dataset should be mandatory.

The issues is that we are not uncovering DASK and other issues when using trivial datasets. Testing MG on a dataset with 2K edges is pointless

Describe your ideal solution

Create a MG dataset that every MG test uses.
Update all test to use the new dataset

Describe any alternatives you have considered

No response

Additional context

No response

Code of Conduct

  • I agree to follow cuGraph's Code of Conduct
  • I have searched the open feature requests and have found no duplicates for this feature request
@BradReesWork BradReesWork added feature request New feature or request ? - Needs Triage Need team to review and classify and removed ? - Needs Triage Need team to review and classify labels Feb 14, 2023
@rlratzel
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rlratzel commented May 9, 2023

Related: #3540 adds MG benchmarks that use RMAT-generated datasets that scale with the number of GPUs.

@rlratzel rlratzel mentioned this issue May 19, 2023
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rapids-bot bot pushed a commit that referenced this issue May 20, 2023
@rlratzel
Updates pytest benchmarks to use synthetic data and multi-GPUs (#3540)
272e316 
closes #2810 
closes #3282

* Adds the ability to use datasets read from files on disk and/or RMAT-generated synthetic datasets.
* Adds markers for "file_data" and "rmat_data" for use by benchmark scripts, based on cluster size.
* Adds CLI options for specifying the RMAT scale and edgefactor in order to generate datasets large enough for MNMG runs.
* Adds fixtures for use by `bench_algos.py` benchmarks which will instantiate graph objs based on dataset type and SG or MG markers.
* Updated `Dataset` class to allow instances to be used as test params and properly provide human-readable/deterministic test IDs.
* Added ability for `Dataset` ctor to take a .csf file as input, useful when a metadata.yaml file for a dataset has not been created yet.
* Added options to `get_test_data.sh` in the CI scripts to download a subset of datasets for C++ (to save time/space since most datasets aren't needed), and to only download the benchmark data for python (for use when running benchmarks as tests).

Authors:
  - Rick Ratzel (https://github.com/rlratzel)
  - Alex Barghi (https://github.com/alexbarghi-nv)

Approvers:
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Vibhu Jawa (https://github.com/VibhuJawa)
  - Ray Douglass (https://github.com/raydouglass)

URL: #3540
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Updates pytest benchmarks to use synthetic data and multi-GPUs rlratzel/cugraph
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