This page describes various use cases that people may have for automated performance tests.
One reason for describing these use cases is to document how well they are served by Fuchsia's tools. Some of these use cases are better served by Fuchsia's current tools than others.
A broader reason for describing these use cases is to help us communicate about testing. The use cases one person has in mind for testing may be different from the use cases another person has in mind.
If a person has some use cases in mind when writing a test, it may be useful to state the use cases explicitly when sending the test to a reviewer for code review, or when asking for help.
The use cases for a test are related to how we treat regressions in its results, which in turn is affected by limitations in how we measure performance and limitations of the statistical tests we use for detecting regressions and improvements.
For example, a large set of tests that is useful for comparing cases (see below) may produce a high rate of false regression warnings due to the multiple comparisons problem in statistics. This set of tests might therefore not be very useful for detecting regressions. We might find that only large regressions are actionable, while small regression warnings are usually spurious and can be ignored.
Some of the use cases described here overlap -- they are not meant to be mutually exclusive.
Detecting regressions, either post-commit or pre-commit: We mostly do this post-commit using Chromeperf and the culprit finder tools. Pre-commit detection is opt-in only, via perfcompare -- it is not applied by default.
- Detecting gradual regressions (creep), resulting from the cumulative effect of many changes. We currently do not have any automated tools for doing this. Chromeperf's detection algorithm only looks for regressions introduced by a single revision or at a single point in time.
Testing potential improvements: That is, testing the effect of changes that attempt to improve performance. This can be done using perfcompare.
Comparing cases: That is, comparing the relative performance of related test cases.
This can be used to look for cases that perform unexpectedly badly relative to others, because we may want to fix those cases. As an example, the performance tests for FIDL encoding and decoding have this as a use case.
This can also be used to measure the costs of operations or subsystems without using profiling. For example, the IPC round trip microbenchmarks measure the round trip time between threads or processes using various different kernel and userland IPC operations. By testing this with and without using FIDL, we can estimate the overhead that FIDL and other userland libraries add on top of the kernel IPC primitives. Similarly, by testing IPC between processes and between threads within a process, we can estimate the cost of a context switch that switches between address spaces.
Providing clues about other regressions: A regression in metric A might not be something we care about as such, but it might be useful in providing a clue about the cause of a regression in metric B. This use case is similar to profiling, but more general.
For example, if the frames per second metric has regressed, we can look at the frame build time metric to see whether that also changed.
Profiling: That is, analyzing the breakdown of time or memory usage within a test.
It is common to use Fuchsia's tracing system for examining the breakdown of time usage, for either automated tests or manual tests. (For this, automated tests have the benefit over manual tests of being more reproducible and less work to run.) However, Fuchsia's tracing system has two differences from statistical profiling tools like perf and OProfile:
- Tracing only records time usage for code that has annotated to produce trace events.
- The typical uses for traces are to inspect them manually or to extract from them a fixed set of metrics (such as frames per second and frame build times). We don't yet have tools for generating more open-ended sets of statistics of the kind usually produced by profiling tools.
Note that the infrastructure around
fuchsiaperffiles is not well suited to recording profiling data. It is not well suited to recording large numbers of metrics describing the breakdown of time or memory usage.
Informing design decisions: The performance characteristics of a subsystem inform how we use it. If the subsystem is slow, we might avoid it, build a layer on top of it (such as caching), or work around it in some other way.
An example of this use case is the "Latency numbers every programmer should know" table. See this recent version of the table.
An early version of this table appears in a talk by Jeff Dean (Stanford CS295 class lecture, spring, 2007) in which he advocates writing microbenchmarks for building intuition about performance and for using as a basis for performance estimates. Various updated versions of this table exist; see this Stack Exchange question for further discussion.