tgravescs opened a new pull request #27138: [SPARK-30448][Core] accelerator aware scheduling enforce cores as limiting resource URL: https://github.com/apache/spark/pull/27138 <!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> This PR is to make sure cores is the limiting resource when using accelerator aware scheduling and fix a few issues with SparkContext.checkResourcesPerTask For the first version of accelerator aware scheduling(SPARK-27495), the SPIP had a condition that we can support dynamic allocation because we were going to have a strict requirement that we don't waste any resources. This means that the number of number of slots each executor has could be calculated from the number of cores and task cpus just as is done today. Somewhere along the line of development we relaxed that and only warn when we are wasting resources. This breaks the dynamic allocation logic if the limiting resource is no longer the cores. This means we will request less executors then we really need to run everything. We have to enforce that cores is always the limiting resource so we should throw if its not. The only issue with us enforcing this is on cluster managers (standalone and mesos coarse grained) where we don't know the executor cores up front by default. Meaning the spark.executor.cores config defaults to 1 but when the executor is started by default it gets all the cores of the Worker. So we have to add logic specifically to handle that and we can't enforce this requirements, we can just warn when dynamic allocation is enabled for those. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> Bug in dynamic allocation if cores is not limiting resource and warnings not correct. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> no ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Unit test added and manually tested the confiditions on local mode, local cluster mode, standalone mode, and yarn.
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