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[EBPF] refactored gpu probe to decouple init and start phases #30615

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merged 15 commits into from
Nov 6, 2024

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@val06 val06 commented Oct 30, 2024

What does this PR do?

decouples "start phase" logic from the "init phase" for the gpu module.
subsequent PRs will do further decoupling of some of the internal structs and fields of the gpu probe

Motivation

  • better code structure
  • will help in this PR to init properly the ebpf resources

Describe how to test/QA your changes

the refactor covered by existing UTs of the gpu pkg

Possible Drawbacks / Trade-offs

Additional Notes

Jira ticket

@val06 val06 added changelog/no-changelog team/ebpf-platform qa/done QA done before merge and regressions are covered by tests labels Oct 30, 2024
@val06 val06 requested a review from a team as a code owner October 30, 2024 15:32
@github-actions github-actions bot added component/system-probe medium review PR review might take time labels Oct 30, 2024
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cit-pr-commenter bot commented Oct 30, 2024

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 78fda898-d0c0-474c-996e-fa8544d9f990

Baseline: fb7c383
Comparison: 750171b
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
basic_py_check % cpu utilization +3.17 [-0.76, +7.09] 1 Logs
idle memory utilization +0.79 [+0.74, +0.83] 1 Logs bounds checks dashboard
quality_gate_idle memory utilization +0.35 [+0.30, +0.40] 1 Logs bounds checks dashboard
uds_dogstatsd_to_api_cpu % cpu utilization +0.13 [-0.61, +0.87] 1 Logs
file_to_blackhole_1000ms_latency egress throughput +0.07 [-0.42, +0.56] 1 Logs
file_to_blackhole_500ms_latency egress throughput +0.05 [-0.19, +0.30] 1 Logs
file_to_blackhole_0ms_latency egress throughput +0.05 [-0.28, +0.38] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.04 [-0.19, +0.26] 1 Logs
idle_all_features memory utilization +0.01 [-0.10, +0.11] 1 Logs bounds checks dashboard
tcp_dd_logs_filter_exclude ingress throughput -0.00 [-0.01, +0.01] 1 Logs
uds_dogstatsd_to_api ingress throughput -0.01 [-0.09, +0.06] 1 Logs
file_to_blackhole_300ms_latency egress throughput -0.06 [-0.23, +0.12] 1 Logs
tcp_syslog_to_blackhole ingress throughput -0.11 [-0.17, -0.05] 1 Logs
file_tree memory utilization -0.23 [-0.36, -0.11] 1 Logs
quality_gate_idle_all_features memory utilization -0.45 [-0.55, -0.35] 1 Logs bounds checks dashboard
pycheck_lots_of_tags % cpu utilization -1.18 [-4.68, +2.32] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
idle memory_usage 9/10 bounds checks dashboard
quality_gate_idle memory_usage 9/10 bounds checks dashboard
file_to_blackhole_0ms_latency memory_usage 10/10
file_to_blackhole_1000ms_latency memory_usage 10/10
file_to_blackhole_100ms_latency memory_usage 10/10
file_to_blackhole_300ms_latency memory_usage 10/10
file_to_blackhole_500ms_latency memory_usage 10/10
idle_all_features memory_usage 10/10 bounds checks dashboard
quality_gate_idle_all_features memory_usage 10/10 bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

  • ✅ = significantly better comparison variant performance
  • ❌ = significantly worse comparison variant performance
  • ➖ = no significant change in performance

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

  1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

  2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

  3. Its configuration does not mark it "erratic".

@val06 val06 force-pushed the valeri.pliskin/refactor-gpu-probe branch from 72ad447 to 992fe29 Compare November 5, 2024 10:04
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agent-platform-auto-pr bot commented Nov 5, 2024

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=48347710 --os-family=ubuntu

Note: This applies to commit 750171b

@val06 val06 force-pushed the valeri.pliskin/refactor-gpu-probe branch from 30425b9 to 10f9aa6 Compare November 6, 2024 13:39
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val06 and others added 4 commits November 6, 2024 15:49
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val06 commented Nov 6, 2024

/merge

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dd-devflow bot commented Nov 6, 2024

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!

@dd-mergequeue dd-mergequeue bot merged commit d3792a2 into main Nov 6, 2024
290 of 292 checks passed
@dd-mergequeue dd-mergequeue bot deleted the valeri.pliskin/refactor-gpu-probe branch November 6, 2024 15:36
@github-actions github-actions bot added this to the 7.61.0 milestone Nov 6, 2024
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