Accelerator's count and read_thread's count were used as criteria for sweep evaluation, but the results are confusing.
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- Python
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Description
I ran evaluations while varying the number of accelerators and the number of read threads. I expected the I/O throughput to scale proportionally with accelerator × read_thread, but as shown below the performance drops dramatically when I use five accelerators. Even if I keep increasing the number of accelerators and read threads, I thought the system would eventually hit a bottleneck, yet instead of a gradual bottleneck the performance collapses.
For the tests I only added the read_thread option to the default run command you provided, and before each run I re‑formatted the NVMe device and remounted the XFS filesystem (the initialization step).
Could you help me figure out why I’m getting these results? Is there something wrong with my setup?
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Research direction
No files, tests, or entry points are named. Start by reproducing the default run with read_thread, five accelerators, and the stated NVMe reformat/XFS remount initialization, then compare the sweep results across accelerator and read_thread counts. Done means identifying whether the collapse comes from the setup or benchmark behavior and documenting the cause.
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Assessment
- Tech stack
- python
- Domain
- performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 28/100