Prefer the use of FxHash over Hash

[blt]

What does this PR do?

This commit adjusts more of our codebase to use the FxHash instead of the randomly seeded Hash{Map|Set}. We want to avoid sources of non-determinism. We believe many of our dependencies will use randomly seeded HashMaps and this commit does nothing to address that.

Related issues

REF SMP-687

[github-actions]

Regression Detector Results

Run ID: f8e119f4-fbcf-47ac-95b7-eb27e7a8719c
Baseline: 3dade50
Comparison: 72c02f9
Total lading-target CPUs: 4

Explanation

A regression test is an integrated performance test for lading-target in a repeatable rig, with varying configuration for lading-target. What follows is a statistical summary of a brief lading-target run for each configuration across SHAs given above. The goal of these tests are to determine quickly if lading-target performance is changed and to what degree by a pull request.

Because a target's optimization goal performance in each experiment will vary somewhat each time it is run, we can only estimate mean differences in optimization goal relative to the baseline target. We express these differences as a percentage change relative to the baseline target, denoted "Δ mean %". These estimates are made to a precision that balances accuracy and cost control. We represent this precision as a 90.00% confidence interval denoted "Δ mean % CI": there is a 90.00% chance that the true value of "Δ mean %" is in that interval.

We decide whether a change in performance is a "regression" -- a change worth investigating further -- if both of the following two criteria are true:

1. The estimated |Δ mean %| ≥ 5.00%. This criterion intends to answer the question "Does the estimated change in mean optimization goal performance have a meaningful impact on your customers?". We assume that when |Δ mean %| < 5.00%, the impact on your customers is not meaningful. We also assume that a performance change in optimization goal is worth investigating whether it is an increase or decrease, so long as the magnitude of the change is sufficiently large.

2. Zero is not in the 90.00% confidence interval "Δ mean % CI" about "Δ mean %". This statement is equivalent to saying that there is at least a 90.00% chance that the mean difference in optimization goal is not zero. This criterion intends to answer the question, "Is there a statistically significant difference in mean optimization goal performance?". It also means there is no more than a 10.00% chance this criterion reports a statistically significant difference when the true difference in mean optimization goal is zero -- a "false positive". We assume you are willing to accept a 10.00% chance of inaccurately detecting a change in performance when no true difference exists.

The table below, if present, lists those experiments that have experienced a statistically significant change in mean optimization goal performance between baseline and comparison SHAs with 90.00% confidence OR have been detected as newly erratic. Negative values of "Δ mean %" mean that baseline is faster, whereas positive values of "Δ mean %" mean that comparison is faster. Results that do not exhibit more than a ±5.00% change in their mean optimization goal are discarded. An experiment is erratic if its coefficient of variation is greater than 0.1. The abbreviated table will be omitted if no interesting change is observed.

No interesting changes in experiment optimization goals with confidence ≥ 90.00% and |Δ mean %| ≥ 5.00%.

Fine details of change detection per experiment.

experiment	goal	Δ mean %	Δ mean % CI	confidence
blackhole_from_apache_common_http	ingress throughput	+0.29	[+0.24, +0.34]	100.00%
apache_common_http_both_directions_this_doesnt_make_sense	ingress throughput	-0.21	[-0.24, -0.19]	100.00%

[goxberry]

For this sort of change, I'm wondering if we should be doing another kind of analysis. If we make this change to decrease non-determinism -- which I agree is preferable -- is that decrease something we should be quantifying in some meaningful way? (Decreased variance in throughput? Change in distribution of throughputs?)

[blt]

For this sort of change, I'm wondering if we should be doing another kind of analysis. If we make this change to decrease non-determinism -- which I agree is preferable -- is that decrease something we should be quantifying in some meaningful way? (Decreased variance in throughput? Change in distribution of throughputs?)

I think this is a really important question and it's not something I have an answer for. But I would like to.