![]() While this is the best way toĮnsure stable performance, it isn't supported on most devices, due to requiring Gradle, or can be applied manually in CI. It is applied automatically when running Microbenchmarks with Never get high enough to heat up the device, or low if a benchmark isn't fully Locking clocks is the best way to get stable performance. Make your benchmark numbers vary widely, so the library provides ways to deal Low state (to save power, or when the device gets hot). ![]() Obtain consistent benchmarksĬlocks on mobile devices dynamically change from high state (for performance) to Measuring higher-level user interactions, like the app launch and scrolling To benchmark this sort of code, we recommend using Microbenchmark-where it runs in a tight loop-isn't a realistic way to measure Because of that, benchmarking this code with Infrequently-run codeĬode that's run once during application startup is not very likely to get JITĬompiled by Android Runtime (ART). When measuring file system performance, this may be difficult because the OSĬaches the file system while in a loop. You can pass different layout parameters in each loop. For example, a custom view's layoutīenchmark might measure only the performance of the layout cache. Run benchmarks in Continuous Integration. To learn how to use the library in a continuous integration (CI) environment, refer to Or performs differently when called multiple times, may not be a good fit for Because benchmarks run in a loop, any code that isn't run frequently, Other types of code are more difficult to measure with the Microbenchmark One item shown at a time, data conversions/processing, and other pieces of code Good examples are RecyclerView scrolling with Microbenchmarks are most useful for CPU work that is run many times in your app,Īlso known as hot code paths. These could be running on a low-priority thread, sleeping due toĭisk access, or unexpectedly calling into an expensive function, like bitmap It can also expose why the operations are slow by showing what is happening This helps you find expensive operations that are worth optimizing. We recommend to profile your code before writing aīenchmark. ![]() Warmup, measures your code performance and allocation counts, and outputsīenchmarking results to both the Android Studio console and a Native code (Kotlin or Java) from within Android Studio. The Jetpack Microbenchmark library allows you to quickly benchmark your Android
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