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[EBPF] gpu: add event collector for debugging #33974
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Uncompressed package size comparisonComparison with ancestor Diff per package
Decision |
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: fdf07f6 Optimization Goals: ✅ No significant changes detected
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | file_to_blackhole_500ms_latency | egress throughput | +0.11 | [-0.68, +0.90] | 1 | Logs |
➖ | quality_gate_idle | memory utilization | +0.03 | [-0.01, +0.06] | 1 | Logs bounds checks dashboard |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.02, +0.03] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.01 | [-0.96, +0.94] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | -0.01 | [-0.65, +0.62] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | -0.01 | [-0.96, +0.93] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.02 | [-0.30, +0.25] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | -0.04 | [-0.70, +0.61] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.06 | [-0.97, +0.85] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.21 | [-0.98, +0.56] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.34 | [-0.80, +0.12] | 1 | Logs |
➖ | file_tree | memory utilization | -0.61 | [-0.67, -0.55] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.68 | [-1.56, +0.21] | 1 | Logs |
➖ | quality_gate_idle_all_features | memory utilization | -0.71 | [-0.77, -0.64] | 1 | Logs bounds checks dashboard |
➖ | tcp_syslog_to_blackhole | ingress throughput | -0.86 | [-0.94, -0.78] | 1 | Logs |
➖ | quality_gate_logs | % cpu utilization | -1.94 | [-4.98, +1.10] | 1 | Logs |
Bounds Checks: ✅ Passed
perf | experiment | bounds_check_name | replicates_passed | links |
---|---|---|---|---|
✅ | file_to_blackhole_0ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency_linear_load | memory_usage | 10/10 | |
✅ | file_to_blackhole_100ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_300ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_300ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_500ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 | |
✅ | quality_gate_idle | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_logs | intake_connections | 10/10 | |
✅ | quality_gate_logs | lost_bytes | 10/10 | |
✅ | quality_gate_logs | memory_usage | 10/10 |
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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
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reviewed
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv aws.create-vm --pipeline-id=55709276 --os-family=ubuntu Note: This applies to commit b84c859 |
Static quality checks ✅Please find below the results from static quality gates Info
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/merge |
View all feedbacks in Devflow UI.
The median merge time in
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What does this PR do?
This PR adds an event collector endpoint to the GPU monitoring module, which collects a set number of consumed events and returns them as JSON-lines data (one event JSON per line).
This endpoint can be queried using the following command line
sudo curl --unix-socket $DD_SYSPROBE_SOCKET http://unix/gpu/debug/collect-events\?count\=N
whereN
is the number of events to collect.Motivation
Allow us to collect data from deployed scenarios for debugging and testing without having to replicate the entire monitored setup.
Describe how you validated your changes
Manually queried and checked the endpoint works multiple times and that parsing of data doesn't stop.
Possible Drawbacks / Trade-offs
Additional Notes