Rethinking Android Runtime with Profile-Guided Optimizations | by Julius Uy | Nov, 2022 | Tech Ready

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Current discuss Android runtime optimizations has to do with a way referred to as profile-guided optimization. (or profile pushed suggestions) The concept behind that is that the software program engineer can contemplate varied code paths {that a} consumer would usually undergo and precompile these paths as a substitute of leaving them as much as the JIT compiler. That is actually attention-grabbing, however not with out caveats. However earlier than we dive into that, we have to do a fast overview of how Java works.

What makes Java interoperable throughout varied CPU and working system architectures is due to the JVM. The best way Java compilers work is that they convert Java supply code to bytecode. The bytecode is then interpreted by the JVM and transformed to machine code relying on the working system and CPU structure it’s operating on. Here’s what the entire course of appears to be like like:


An Android engineer would possibly now know that Android doesn’t use the JVM. As a result of Android runs on house and reminiscence constrained gadgets, Dalvik is used as a substitute (and since Lollipop, ART)¹ Under is the modified Android construct and code execution path:


In contrast to Dalvik, which relied on JIT (just-in-time compilation), Android’s runtime relied closely on AOT (forward of compilation). JIT works by changing Java bytecode into machine code at runtime. This permits Java to stay interoperable and usable, particularly in storage-constrained environments.

One would possibly surprise, “if JIT compiled the code at runtime, would not or not it’s sluggish?” The reply is sure. JIT sacrifices efficiency for storage. (Not solely true. Extra on that later) In fact, a counter-proposal have to be raised. At a time when storage is now so low cost, can we optimize efficiency over storage? Those that thought so would flock to the Forward of Time Compilation (AOT). On this method, Java precompiles varied components of the code in order that the JIT doesn’t need to take that duty. The draw back, in fact, is that as a result of AOT code should run on varied working programs and CPU architectures, all of those precompiled machine code have to be bundled into the identical Java bundle, which will increase the file measurement.

This isn’t an issue when the working system isn’t up to date regularly. Nonetheless, as a result of Android and its OEMs usually patch their working system, a pre-compile have to be carried out each time there may be an replace. This results in a big quantity of ready time for customers who’ve simply upgraded their telephones.

That is why I can not play Genshin Affect with a smile on my face.

Now let’s return to JIT for a second. Newer JIT implementations include an execution counter in order that it is aware of which a part of the code path to pre-compile, thereby bettering efficiency. In fact, that is based mostly on how the person consumer interacts with the app. Now, suppose the JIT has entry to the cloud, so it might get the code paths utilized by varied customers world wide. May you, in concept, pre-optimize execution for the consumer? The reply is sure. Now that the cloud is extensively obtainable, JIT can depend on crowdsourced knowledge within the cloud to make purposes run even sooner. For Android, Google calls this Cloud Profiles.²

Nonetheless, Cloud Profiles has an issue. Whereas they excel at figuring out paths of execution, they’re solely pretty much as good as the appliance model lifetime. When the app is up to date, the cloud profiles for the outdated model of the app develop into ineffective as proven under:


It’s because Cloud Profiles is not going to have knowledge on how the brand new model of the appliance is operating. Due to this fact, you can’t pre-optimize for Early Entry customers. In fact, this may reduce over time, however the issue persists for early adopters, and if the enterprise does weekly deployments, like Twitter and Netflix, the advantages of cloud profiles are decreased.

Android not too long ago launched profile-guided optimizations utilizing macrobenchmarking and benchmark profiles. That is to unravel the issue talked about above. In essence, the concept is to seek out the precise stability between JIT and AOT in order that solely code paths that the software program engineer thinks customers usually go to are optimized. If the AOT codes include the app, then one can discover the precise stability between having the right AAB file measurement and in addition making the app work as easily as potential. However there’s an issue.

Think about Seize, which shows varied residence screens relying on the nation you are in:

Seize the house web page in Singapore, Vietnam and Indonesia.

Right here, profile-guided optimizations lose their attraction. As a result of now, the delivery profiles linked to every nation are typically sub-optimal. Additionally, when the use case branches into a number of options that customers can use in an excellent app, the worth of profile-driven optimizations rapidly diminishes.

In truth, this 2019 discuss identified that AOT tends to be nearly all the time suboptimal in comparison with JIT as a result of it depends on extremely particular code paths that won’t all the time maintain true in precise use.

Given sufficient time, JIT will all the time outperform AOT because it has a bigger quantity of information to make use of. Additionally, one would not need to predict the vary of code paths that customers will undergo earlier than truly going by way of them.

Ultimately, all isn’t misplaced. Since most Android apps include Firebase and run on gadgets operating Google Play Companies, these two might be merged and ship generally used knowledge to the cloud, identical to Firebase Analytics, Firebase Take a look at Lab, and Android already do. vitals. A consumer’s typical utilization patterns is not going to differ between variations of the appliance, assuming the means to take action stay intact, knowledge from these companies will help carry out automated profile-driven optimizations based mostly on giant volumes of information. Due to this fact, extra optimizations for software efficiency are prone to happen on the Google Play aspect, the place cloud profiles will attempt to pre-optimize areas of optimum use which might be customized in nature. This, in fact, is a storage hog.³ Nonetheless, now that storage is considerably cheaper than it was once, incremental customization of this runtime optimization is actually not far-fetched.

In conclusion, it is suggested to remember using primary profiles even when the latest Android Developer Summit and varied on-line sources encourage it. For Southeast Asia, the place tremendous apps are fairly widespread, this can lead to vital human capital expenditure with little to no profit. If Google Maps elevated searches by simply 2.4% and is a high funnel exercise, the advance in important conversion metrics like a product buy is even a lot smaller. So if it is not price spending tons of and even hundreds of {dollars} in man-hours for referral profiles, I like to recommend holding again. Cloud Profiles would be the factor that will save the day, and also you hardly need to carry a finger.

Suzanne!!! The distant management has no battery!


¹ Technically, you’ll be able to allow ART in KitKat by way of Developer Choices, however it did not develop into the default choice till Lollipop.

² In fact, the Java world has a number of alternate options resembling JITaaS.

³ However Google simply gave everybody 1TB free storage, in order that’s about it.

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Rethinking Android Runtime with Profile-Guided Optimizations | by Julius Uy | Nov, 2022