chore(deps): Bump crossbeam-utils from 0.8.19 to 0.8.20 in the crossbeam group

[dependabot]

Bumps the crossbeam group with 1 update: crossbeam-utils.

Updates crossbeam-utils from 0.8.19 to 0.8.20

Release notes

Sourced from crossbeam-utils's releases.

crossbeam-utils 0.8.20

• Implement Display for CachePadded. (#1097)

Commits

• 3f5d51f Prepare for the next release
• a9d236c Use setup-cross-toolchain-action instead of cross
• 4a514f4 Address unexpected_cfgs warning
• 91edb88 Add select_biased! macro (#1040)
• d1ab079 Fix clippy::assigning_clones warning
• 3045680 ci: Set timeout-minutes in all jobs
• 1dc72c8 Revert "ci: Pin nightly toolchain for sanitizers to nightly-2024-03-13"
• 39417a5 ci: Use taiki-e/checkout-action action
• 120a6dd Run cargo-careful in CI
• 2b06585 impl Display for CachePadded<T> (#1097)
• Additional commits viewable in compare view

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[github-actions]

Regression Detector Results

Run ID: 6afda2bb-33e0-45d4-b3ed-e048c79c38f6
Baseline: d5c23fe
Comparison: eacf1e8
Total CPUs: 7

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

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	datadog_agent_remap_blackhole	ingress throughput	+2.08	[+1.93, +2.23]
➖	syslog_regex_logs2metric_ddmetrics	ingress throughput	+1.93	[+1.86, +2.01]
➖	syslog_log2metric_tag_cardinality_limit_blackhole	ingress throughput	+1.86	[+1.74, +1.98]
➖	datadog_agent_remap_datadog_logs	ingress throughput	+1.48	[+1.37, +1.58]
➖	datadog_agent_remap_blackhole_acks	ingress throughput	+1.20	[+1.10, +1.31]
➖	fluent_elasticsearch	ingress throughput	+1.14	[+0.65, +1.64]
➖	datadog_agent_remap_datadog_logs_acks	ingress throughput	+0.75	[+0.67, +0.84]
➖	socket_to_socket_blackhole	ingress throughput	+0.69	[+0.61, +0.77]
➖	splunk_hec_route_s3	ingress throughput	+0.61	[+0.16, +1.06]
➖	http_to_http_acks	ingress throughput	+0.53	[-0.83, +1.89]
➖	http_elasticsearch	ingress throughput	+0.43	[+0.35, +0.50]
➖	http_to_s3	ingress throughput	+0.16	[-0.12, +0.44]
➖	http_to_http_noack	ingress throughput	+0.13	[+0.04, +0.23]
➖	http_to_http_json	ingress throughput	+0.06	[-0.02, +0.14]
➖	otlp_grpc_to_blackhole	ingress throughput	+0.04	[-0.05, +0.13]
➖	splunk_hec_to_splunk_hec_logs_acks	ingress throughput	+0.00	[-0.14, +0.14]
➖	splunk_hec_indexer_ack_blackhole	ingress throughput	+0.00	[-0.15, +0.15]
➖	splunk_hec_to_splunk_hec_logs_noack	ingress throughput	-0.04	[-0.15, +0.08]
➖	otlp_http_to_blackhole	ingress throughput	-0.07	[-0.18, +0.04]
➖	enterprise_http_to_http	ingress throughput	-0.11	[-0.19, -0.03]
➖	syslog_humio_logs	ingress throughput	-0.34	[-0.47, -0.20]
➖	syslog_log2metric_splunk_hec_metrics	ingress throughput	-0.64	[-0.78, -0.50]
➖	syslog_splunk_hec_logs	ingress throughput	-0.77	[-0.84, -0.71]
➖	syslog_loki	ingress throughput	-0.88	[-0.93, -0.83]
➖	http_text_to_http_json	ingress throughput	-0.98	[-1.12, -0.84]
➖	syslog_log2metric_humio_metrics	ingress throughput	-1.74	[-1.83, -1.65]
➖	file_to_blackhole	egress throughput	-2.23	[-4.67, +0.21]

Explanation

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".