Significant Performance Variability Across Nodes in Spark Cluster with Version 0.5.0
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- Dominant language
- Java
- Stars
- 928
- Forks
- 227
- PR merge metrics
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Description
I've been using version 0.5.0 and observed some performance inconsistencies across different nodes in my Spark cluster. Specifically, some nodes execute tasks significantly faster than others, with the difference in execution times ranging from tens to thousands of times slower on certain nodes.
Given this situation, I'm curious to know if there are any CPU-specific optimizations made during the compilation of this library. For instance, are there optimizations that favor Intel CPUs over AMD CPUs, which might explain the observed performance disparity?
Any insights or suggestions on this matter would be greatly appreciated.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No source files, tests, or entry points are named. Start by comparing the version 0.5.0 build and task execution across the affected Spark nodes and their CPU types; done means identifying whether compilation or CPU-specific optimization explains the reported variability.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java, spark, tensorflow
- Domain
- distributed-systems, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100