turn out to be a greater Android developer with Compiler Explorer

Posted by Shai Barack – Android Platform Efficiency lead

Introducing Android assist in Compiler Explorer

In a earlier weblog put up you discovered how Android engineers constantly enhance the Android Runtime (ART) in ways in which enhance app efficiency on person gadgets. These adjustments to the compiler make system and app code quicker or smaller. Builders don’t want to vary their code and rebuild their apps to learn from new optimizations, and customers get a greater expertise. On this weblog put up I’ll take you contained in the compiler with a instrument referred to as Compiler Explorer and witness a few of these optimizations in motion.

Compiler Explorer is an interactive web site for learning how compilers work. It’s an open supply venture that anybody can contribute to. This 12 months, our engineers added assist to Compiler Explorer for the Java and Kotlin programming languages on Android.

You should utilize Compiler Explorer to grasp how your supply code is translated to meeting language, and the way high-level programming language constructs in a language like Kotlin turn out to be low-level directions that run on the processor.

At Google our engineers use this instrument to check completely different coding patterns for effectivity, to see how present compiler optimizations work, to share new optimization alternatives, and to show and be taught.
Studying is greatest when it’s performed via instruments, not guidelines. As a substitute of instructing builders to memorize completely different guidelines for the right way to write environment friendly code or what the compiler would possibly or may not optimize, give the engineers the instruments to seek out out for themselves what occurs once they write their code in several methods, and allow them to experiment and be taught. Let’s be taught collectively!

Begin by going to godbolt.org. By default we see C++ pattern code, so click on the dropdown that claims C++ and choose Android Java. It is best to see this pattern code:

class Sq. {
   static int sq.(int num) {
       return num * num;
   }
}

On the left you’ll see a quite simple program. You would possibly say that it is a one line program. However this isn’t a significant assertion when it comes to efficiency – what number of strains of code there are doesn’t inform us how lengthy this program will take to run, or how a lot reminiscence will likely be occupied by the code when this system is loaded.

On the appropriate you’ll see a disassembly of the compiler output. That is expressed when it comes to meeting language for the goal structure, the place each line is a CPU instruction. Trying on the directions, we are able to say that the implementation of the sq.(int num) methodology consists of two directions within the goal structure. The quantity and kind of directions give us a greater concept for how briskly this system is than the variety of strains of supply code. Because the goal structure is AArch64 aka ARM64, each instruction is 4 bytes, which implies that our program’s code occupies 8 bytes in RAM when this system is compiled and loaded.

Let’s take a quick detour and introduce some Android toolchain ideas.

The Android construct toolchain (in short)

Whenever you write your Android app, you’re usually writing supply code within the Java or Kotlin programming languages. Whenever you construct your app in Android Studio, it’s initially compiled by a language-specific compiler into language-agnostic JVM bytecode in a .jar. Then the Android construct instruments remodel the .jar into Dalvik bytecode in .dex recordsdata, which is what the Android Runtime executes on Android gadgets. Usually builders use d8 of their Debug builds, and r8 for optimized Launch builds. The .dex recordsdata go within the .apk that you simply push to check gadgets or add to an app retailer. As soon as the .apk is put in on the person’s system, an on-device compiler which is aware of the particular goal system structure can convert the bytecode to directions for the system’s CPU.

We are able to use Compiler Explorer to find out how all these instruments come collectively, and to experiment with completely different inputs and see how they have an effect on the outputs.

Going again to our default view for Android Java, on the left is Java supply code and on the appropriate is the disassembly for the on-device compiler dex2oat, the final step in our toolchain diagram. The goal structure is ARM64 as that is the commonest CPU structure in use as we speak by Android gadgets.

The ARM64 Instruction Set Structure provides many directions and extensions, however as you learn disassemblies one can find that you simply solely have to memorize just a few key directions. You may search for ARM64 Fast Reference playing cards on-line that will help you learn disassemblies.

At Google we research the output of dex2oat in Compiler Explorer for various causes, akin to:

• Gaining instinct for what optimizations the compiler performs so as to consider the right way to write extra environment friendly code.
• Estimating how a lot reminiscence will likely be required when a program with this snippet of code is loaded into reminiscence.
• Figuring out optimization alternatives within the compiler – methods to generate directions for a similar code which can be extra environment friendly, leading to quicker execution or in decrease reminiscence utilization with out requiring app builders to vary and rebuild their code.
• Troubleshooting compiler bugs! 🐞

Compiler optimizations demystified

Let’s take a look at an actual instance of compiler optimizations in follow. Within the earlier weblog put up you’ll be able to examine compiler optimizations that the ART workforce lately added, akin to coalescing returns. Now you’ll be able to see the optimization, with Compiler Explorer!

Let’s load this instance:

class CoalescingReturnsDemo {
   String intToString(int num) {
       change (num) {
           case 1:
               return "1";
           case 2:
               return "2";
           case 3:
               return "3";           
           default:
               return "different";
       }
   }
}

How would a compiler implement this code in CPU directions? Each case could be a department goal, with a case physique that has some distinctive directions (akin to referencing the particular string) and a few frequent directions (akin to assigning the string reference to a register and returning to the caller). Coalescing returns implies that some directions on the tail of every case physique may be shared throughout all circumstances. The advantages develop for bigger switches, proportional to the variety of the circumstances.

You may see the optimization in motion! Merely create two compiler home windows, one for dex2oat from the October 2022 launch (the final launch earlier than the optimization was added), and one other for dex2oat from the November 2023 launch (the primary launch after the optimization was added). It is best to see that earlier than the optimization, the dimensions of the strategy physique for intToString was 124 bytes. After the optimization, it’s down to only 76 bytes.

That is in fact a contrived instance for simplicity’s sake. However this sample is quite common in Android code. For example think about an implementation of Handler.handleMessage(Message), the place you would possibly implement a change assertion over the worth of Message#what.

How does the compiler implement optimizations akin to this? Compiler Explorer lets us look contained in the compiler’s pipeline of optimization passes. In a compiler window, click on Add New > Choose Pipeline. A brand new window will open, exhibiting the Excessive-level Inside Illustration (HIR) that the compiler makes use of for this system, and the way it’s reworked at each step.

In case you take a look at the code_sinking cross you will note that the November 2023 compiler replaces Return HIR directions with Goto directions.

Many of the passes are hidden when Filters > Disguise Inconsequential Passes is checked. You may uncheck this feature and see all optimization passes, together with ones that didn’t change the HIR (i.e. haven’t any “diff” over the HIR).

Let’s research one other easy optimization, and look contained in the optimization pipeline to see it in motion. Take into account this code:

class ConstantFoldingDemo {
   static int demo(int num) {
       int outcome = num;
       if (num == 2) {
           outcome = num + 2;
       }
       return outcome;
   }
}

The above is functionally equal to the beneath:

class ConstantFoldingDemo {
   static int demo(int num) {
       int outcome = num;
       if (num == 2) {
           outcome = 4;
       }
       return outcome;
   }
}

Can the compiler make this optimization for us? Let’s load it in Compiler Explorer and switch to the Choose Pipeline Viewer for solutions.

The disassembly exhibits us that the compiler by no means bothers with “two plus two”, it is aware of that if num is 2 then outcome must be 4. This optimization is named fixed folding. Contained in the conditional block the place we all know that num == 2 we propagate the fixed 2 into the symbolic identify num, then fold num + 2 into the fixed 4.

You may see this optimization occurring over the compiler’s IR by choosing the constant_folding cross within the Choose Pipeline Viewer.

Kotlin and Java, facet by facet

Now that we’ve seen the directions for Java code, strive altering the language to Android Kotlin. It is best to see this pattern code, the Kotlin equal of the fundamental Java pattern we’ve seen earlier than:

enjoyable sq.(num: Int): Int = num * num

You’ll discover that the supply code is completely different however the pattern program is functionally similar, and so is the output from dex2oat. Discovering the sq. of a quantity leads to the identical directions, whether or not you write your supply code in Java or in Kotlin.

You may take this chance to check attention-grabbing language options and uncover how they work. For example, let’s examine Java String concatenation with Kotlin String interpolation.

In Java, you would possibly write your code as follows:

class StringConcatenationDemo {
   void stringConcatenationDemo(String myVal) {
       System.out.println("The worth of myVal is " + myVal);
   }
}

Let’s learn how Java String concatenation really works by attempting this instance in Compiler Explorer.

First you’ll discover that we modified the output compiler from dex2oat to d8. Studying Dalvik bytecode, which is the output from d8, is often simpler than studying the ARM64 directions that dex2oat outputs. It is because Dalvik bytecode makes use of greater degree ideas. Certainly you’ll be able to see the names of sorts and strategies from the supply code on the left facet mirrored within the bytecode on the appropriate facet. Strive altering the compiler to dex2oat and again to see the distinction.

As you learn the d8 output you could understand that Java String concatenation is definitely applied by rewriting your supply code to make use of a StringBuilder. The supply code above is rewritten internally by the Java compiler as follows:

class StringConcatenationDemo {
   void stringConcatenationDemo(String myVal) {
       StringBuilder sb = new StringBuilder();
       sb.append("The worth of myVal is ");
       sb.append(myVal);
       System.out.println(sb.toString());
  }
}

In Kotlin, we are able to use String interpolation:

enjoyable stringInterpolationDemo(myVal: String) {
   System.out.println("The worth of myVal is $myVal");
}

The Kotlin syntax is less complicated to learn and write, however does this comfort come at a value? In case you do this instance in Compiler Explorer, you could discover that the Dalvik bytecode output is roughly the identical! On this case we see that Kotlin provides an improved syntax, whereas the compiler emits related bytecode.

At Google we research examples of language options in Compiler Explorer to study how high-level language options are applied in lower-level phrases, and to higher inform ourselves on the completely different tradeoffs that we’d make in selecting whether or not and the right way to undertake these language options. Recall our studying precept: instruments, not guidelines. Somewhat than memorizing guidelines for the way you need to write your code, use the instruments that may enable you perceive the upsides and disadvantages of various options, after which make an knowledgeable determination.

What occurs whenever you minify your app?

Talking of constructing knowledgeable choices as an app developer, try to be minifying your apps with R8 when constructing your Launch APK. Minifying usually does three issues to optimize your app to make it smaller and quicker:

1. Lifeless code elimination: discover all of the stay code (code that’s reachable from well-known program entry factors), which tells us that the remaining code isn’t used, and subsequently may be eliminated.

2. Bytecode optimization: numerous specialised optimizations that rewrite your app’s bytecode to make it functionally similar however quicker and/or smaller.

3. Obfuscation: renaming all sorts, strategies, and fields in your program that aren’t accessed by reflection (and subsequently may be safely renamed) from their names in supply code (com.instance.MyVeryLongFooFactorySingleton) to shorter names that slot in much less reminiscence (a.b.c).

Let’s see an instance of all three advantages! Begin by loading this view in Compiler Explorer.

First you’ll discover that we’re referencing sorts from the Android SDK. You are able to do this in Compiler Explorer by clicking Libraries and including Android API stubs.

Second, you’ll discover that this view has a number of supply recordsdata open. The Kotlin supply code is in instance.kt, however there’s one other file referred to as proguard.cfg.

-keep class MinifyDemo {
   public void goToSite(...);
}

Trying inside this file, you’ll see directives within the format of Proguard configuration flags, which is the legacy format for configuring what to maintain when minifying your app. You may see that we’re asking to maintain a sure methodology of MinifyDemo. “Holding” on this context means don’t shrink (we inform the minifier that this code is stay). Let’s say we’re creating a library and we’d like to supply our buyer a prebuilt .jar the place they will name this methodology, so we’re protecting this as a part of our API contract.

We arrange a view that may allow us to see the advantages of minifying. On one facet you’ll see d8, exhibiting the dex code with out minification, and on the opposite facet r8, exhibiting the dex code with minification. By evaluating the 2 outputs, we are able to see minification in motion:

1. Lifeless code elimination: R8 eliminated all of the logging code, because it by no means executes (as DEBUG is at all times false). We eliminated not simply the calls to android.util.Log, but in addition the related strings.

2. Bytecode optimization: because the specialised strategies goToGodbolt, goToAndroidDevelopers, and goToGoogleIo simply name goToUrl with a hardcoded parameter, R8 inlined the calls to goToUrl into the decision websites in goToSite. This inlining saves us the overhead of defining a way, invoking the strategy, and coming back from the strategy.

3. Obfuscation: we advised R8 to maintain the general public methodology goToSite, and it did. R8 additionally determined to maintain the strategy goToUrl because it’s utilized by goToSite, however you’ll discover that R8 renamed that methodology to a. This methodology’s identify is an inside implementation element, so obfuscating its identify saved us just a few treasured bytes.

You should utilize R8 in Compiler Explorer to grasp how minification impacts your app, and to experiment with other ways to configure R8.

At Google our engineers use R8 in Compiler Explorer to check how minification works on small samples. The authoritative instrument for learning how an actual app compiles is the APK Analyzer in Android Studio, as optimization is a whole-program downside and a snippet may not seize each nuance. However iterating on launch builds of an actual app is sluggish, so learning pattern code in Compiler Explorer helps our engineers rapidly be taught and iterate.

Google engineers construct very giant apps which can be utilized by billions of individuals on completely different gadgets, so that they care deeply about these sorts of optimizations, and attempt to take advantage of use out of optimizing instruments. However lots of our apps are additionally very giant, and so altering the configuration and rebuilding takes a really very long time. Our engineers can now use Compiler Explorer to experiment with minification below completely different configurations and see leads to seconds, not minutes.

It’s possible you’ll marvel what would occur if we modified our code to rename goToSite? Sadly our construct would break, except we additionally renamed the reference to that methodology within the Proguard flags. Luckily, R8 now natively helps Preserve Annotations as a substitute for Proguard flags. We are able to modify our program to make use of Preserve Annotations:

@UsedByReflection(form = KeepItemKind.CLASS_AND_METHODS)
public static void goToSite(Context context, String website) {
    ...
}

Right here is the full instance. You’ll discover that we eliminated the proguard.cfg file, and below Libraries we added “R8 keep-annotations”, which is how we’re importing @UsedByReflection.

At Google our engineers want annotations over flags. Right here we’ve seen one good thing about annotations – protecting the details about the code in a single place moderately than two makes refactors simpler. One other is that the annotations have a self-documenting facet to them. For example if this methodology was saved often because it’s referred to as from native code, we’d annotate it as @UsedByNative as a substitute.

Baseline profiles and also you

Lastly, let’s contact on baseline profiles. Up to now you noticed some demos the place we checked out dex code, and others the place we checked out ARM64 directions. In case you toggle between the completely different codecs you’ll discover that the high-level dex bytecode is far more compact than low-level CPU directions. There’s an attention-grabbing tradeoff to discover right here – whether or not, and when, to compile bytecode to CPU directions?

For any program methodology, the Android Runtime has three compilation choices:

1. Compile the strategy Simply in Time (JIT).

2. Compile the strategy Forward of Time (AOT).

3. Don’t compile the strategy in any respect, as a substitute use a bytecode interpreter.

Operating code in an interpreter is an order of magnitude slower, however doesn’t incur the price of loading the illustration of the strategy as CPU directions which as we’ve seen is extra verbose. That is greatest used for “chilly” code – code that runs solely as soon as, and isn’t essential to person interactions.

When ART detects {that a} methodology is “sizzling”, it is going to be JIT-compiled if it’s not already been AOT compiled. JIT compilation accelerates execution instances, however pays the one-time value of compilation throughout app runtime. That is the place baseline profiles are available in. Utilizing baseline profiles, you because the app developer can provide ART a touch as to which strategies are going to be sizzling or in any other case price compiling. ART will use that trace earlier than runtime, compiling the code AOT (often at set up time, or when the system is idle) moderately than at runtime. This is the reason apps that use Baseline Profiles see quicker startup instances.

With Compiler Explorer we are able to see Baseline Profiles in motion.

Let’s open this instance.

The Java supply code has two methodology definitions, factorial and fibonacci. This instance is ready up with a handbook baseline profile, listed within the file profile.prof.txt. You’ll discover that the profile solely references the factorial methodology. Consequently, the dex2oat output will solely present compiled code for factorial, whereas fibonacci exhibits within the output with no directions and a dimension of 0 bytes.

Within the context of compilation modes, which means that factorial is compiled AOT, and fibonacci will likely be compiled JIT or interpreted. It is because we utilized a distinct compiler filter within the profile pattern. That is mirrored within the dex2oat output, which reads: “Compiler filter: speed-profile” (AOT compile solely profile code), the place earlier examples learn “Compiler filter: velocity” (AOT compile every thing).

Conclusion

Compiler Explorer is a good instrument for understanding what occurs after you write your supply code however earlier than it could run on a goal system. The instrument is simple to make use of, interactive, and shareable. Compiler Explorer is greatest used with pattern code, but it surely goes via the identical procedures as constructing an actual app, so you’ll be able to see the affect of all steps within the toolchain.

By studying the right way to use instruments like this to find how the compiler works below the hood, moderately than memorizing a bunch of guidelines of optimization greatest practices, you may make extra knowledgeable choices.

Now that you have seen the right way to use the Java and Kotlin programming languages and the Android toolchain in Compiler Explorer, you’ll be able to degree up your Android growth expertise.

Lastly, do not forget that Compiler Explorer is an open supply venture on GitHub. If there’s a function you’d prefer to see then it is only a Pull Request away.

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