mlcommons / mlcommons/inference

Can we control CPU core usage in MLPerf on NVIDIA Orin?

Open
#2,169 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Python
Stars
1.6k
Forks
650
Avg merge
1d 22h
Merged PRs (30d)
6

Description

Can we control CPU core usage in MLPerf on NVIDIA Orin?

We are running MLPerf on an NVIDIA Orin platform with the following CPU configuration:

  • 12 CPU cores
  • 1 thread per core
  • 3 clusters

Scenario

We tried running MLPerf with different core affinities using taskset. The goal is to limit the benchmark to specific cores and observe the expected core utilization.

Here are our observations:

Command Scenario Observation
taskset -c 0-11 12C 12T All 12 cores active
taskset -c 0-3 4C 12T All 12 cores still active
taskset -c 0 1C 12T All 12 cores active (core switching observed)

We expected only the specified cores (e.g., 0-3) to be active during execution, but all cores are being used regardless of the taskset configuration.

Question

Is it possible to control or limit the CPU cores used by MLPerf (e.g., via taskset or other methods) on the NVIDIA Orin platform?

If yes, how can we configure MLPerf to respect the core affinity settings?

Thanks in advance!

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reproducing the listed taskset commands on the NVIDIA Orin setup and inspect how MLPerf launches benchmark processes and handles CPU affinity. Determine whether the observed core switching is expected or indicates an affinity issue, then document a verified configuration or limitation with results for the 12-, 4-, and 1-core cases.

Written by the indexing model from the issue text.

Assessment

Tech stack
linux, machine-learning, python
Domain
machine-learning, operating-systems, performance
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.