grow 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 previous blog post you realized how Android engineers repeatedly enhance the Android Runtime (ART) in ways in which enhance app efficiency on person gadgets. These modifications to the compiler make system and app code sooner or smaller. Builders don’t want to alter 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 source project 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 know how your supply code is translated to meeting language, and the way high-level programming language constructs in a language like Kotlin grow to be low-level directions that run on the processor.

At Google our engineers use this instrument to check totally 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 executed via instruments, not guidelines. As an alternative of instructing builders to memorize totally different guidelines for 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 after they write their code in numerous 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’s best to see this sample 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 this can be a one line program. However this isn’t a significant assertion when it comes to efficiency – what number of traces of code there are doesn’t inform us how lengthy this program will take to run, or how a lot reminiscence can be occupied by the code when this system is loaded.

On the fitting 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. Wanting on the directions, we are able to say that the implementation of the sq.(int num) technique 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 traces 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 short detour and introduce some Android toolchain ideas.

The Android construct toolchain (in short)

If you write your Android app, you’re usually writing supply code within the Java or Kotlin programming languages. If 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 Release builds. The .dex recordsdata go within the .apk that you just push to check gadgets or add to an app retailer. As soon as the .apk is put in on the person’s machine, an on-device compiler which is aware of the precise goal machine structure can convert the bytecode to directions for the machine’s CPU.

We will use Compiler Explorer to find out how all these instruments come collectively, and to experiment with totally 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 fitting 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 at this time by Android gadgets.

The ARM64 Instruction Set Architecture affords many directions and extensions, however as you learn disassemblies you will discover that you just solely must memorize just a few key directions. You possibly can search for ARM64 Fast Reference playing cards on-line that can assist you learn disassemblies.

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

• Gaining instinct for what optimizations the compiler performs so as to consider write extra environment friendly code.
• Estimating how a lot reminiscence can 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 might be extra environment friendly, leading to sooner execution or in decrease reminiscence utilization with out requiring app builders to alter and rebuild their code.
• Troubleshooting compiler bugs! 🐞

Compiler optimizations demystified

Let’s have a look at an actual instance of compiler optimizations in observe. Within the previous blog post you may examine compiler optimizations that the ART group just lately added, akin to coalescing returns. Now you may see the optimization, with Compiler Explorer!

Let’s load this example:

class CoalescingReturnsDemo {
   String intToString(int num) {
       swap (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 precise string) and a few widespread 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 might be shared throughout all circumstances. The advantages develop for bigger switches, proportional to the variety of the circumstances.

You possibly can 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’s best to see that earlier than the optimization, the scale 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 take into account an implementation of Handler.handleMessage(Message), the place you would possibly implement a swap 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.

For those who have a look at the code_sinking go 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 possibly can 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 examine 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 consequence = num;
       if (num == 2) {
           consequence = num + 2;
       }
       return consequence;
   }
}

The above is functionally equal to the beneath:

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

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 reveals us that the compiler by no means bothers with “two plus two”, it is aware of that if num is 2 then consequence must be 4. This optimization is known as constant 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 possibly can see this optimization occurring over the compiler’s IR by deciding on the constant_folding go within the Choose Pipeline Viewer.

Kotlin and Java, aspect by aspect

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

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

You’ll discover that the supply code is totally 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 possibly can take this chance to check fascinating language options and uncover how they work. For example, let’s evaluate 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 the way Java String concatenation truly works by trying this example 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 normally simpler than studying the ARM64 directions that dex2oat outputs. It is because Dalvik bytecode makes use of larger degree ideas. Certainly you may see the names of sorts and strategies from the supply code on the left aspect mirrored within the bytecode on the fitting aspect. 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 carried out 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? For those who do that instance in Compiler Explorer, you could discover that the Dalvik bytecode output is roughly the identical! On this case we see that Kotlin affords an improved syntax, whereas the compiler emits related bytecode.

At Google we examine examples of language options in Compiler Explorer to study how high-level language options are carried out in lower-level phrases, and to raised inform ourselves on the totally different tradeoffs that we would make in selecting whether or not and undertake these language options. Recall our studying precept: instruments, not guidelines. Moderately than memorizing guidelines for a way you must write your code, use the instruments that may enable you perceive the upsides and disadvantages of various options, after which make an knowledgeable choice.

What occurs once you minify your app?

Talking of constructing knowledgeable selections as an app developer, you have to be minifying your apps with R8 when constructing your Launch APK. Minifying typically does three issues to optimize your app to make it smaller and sooner:

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 will not be used, and subsequently might be eliminated.

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

3. Obfuscation: renaming all sorts, strategies, and fields in your program that aren’t accessed by reflection (and subsequently might 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 may be one other file referred to as proguard.cfg.

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

Wanting 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 possibly can see that we’re asking to maintain a sure technique of MinifyDemo. “Holding” on this context means don’t shrink (we inform the minifier that this code is stay). Let’s say we’re growing a library and we’d like to supply our buyer a prebuilt .jar the place they’ll name this technique, 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 aspect you’ll see d8, exhibiting the dex code with out minification, and on the opposite aspect 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 all the time false). We eliminated not simply the calls to android.util.Log, but additionally 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 technique, invoking the strategy, and coming back from the strategy.

3. Obfuscation: we advised R8 to maintain the general public technique 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 technique to a. This technique’s identify is an inner implementation element, so obfuscating its identify saved us just a few valuable bytes.

You should utilize R8 in Compiler Explorer to know 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 drawback and a snippet may not seize each nuance. However iterating on launch builds of an actual app is gradual, so learning pattern code in Compiler Explorer helps our engineers rapidly be taught and iterate.

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

You might marvel what would occur if we modified our code to rename goToSite? Sadly our construct would break, until we additionally renamed the reference to that technique within the Proguard flags. Happily, R8 now natively helps Keep Annotations as a substitute for Proguard flags. We will 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 complete example. You’ll discover that we eliminated the proguard.cfg file, and beneath Libraries we added “R8 keep-annotations”, which is how we’re importing @UsedByReflection.

At Google our engineers choose annotations over flags. Right here we’ve seen one advantage of annotations – protecting the details about the code in a single place quite than two makes refactors simpler. One other is that the annotations have a self-documenting facet to them. For example if this technique was stored usually 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. For those who toggle between the totally different codecs you’ll discover that the high-level dex bytecode is rather more compact than low-level CPU directions. There’s an fascinating tradeoff to discover right here – whether or not, and when, to compile bytecode to CPU directions?

For any program technique, the Android Runtime has three compilation options:

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 important to person interactions.

When ART detects {that a} technique is “sizzling”, it will likely be JIT-compiled if it’s not already been AOT compiled. JIT compilation accelerates execution instances, however pays the one-time price 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 (normally at set up time, or when the machine is idle) quite than at runtime. That is why apps that use Baseline Profiles see faster startup times.

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

Let’s open this example.

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

Within the context of compilation modes, because of this factorial is compiled AOT, and fibonacci can be compiled JIT or interpreted. It is because we utilized a unique 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: pace” (AOT compile every little thing).

Conclusion

Compiler Explorer is a good instrument for understanding what occurs after you write your supply code however earlier than it might run on a goal machine. The instrument is straightforward 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 may see the affect of all steps within the toolchain.

By studying use instruments like this to find how the compiler works beneath the hood, quite than memorizing a bunch of guidelines of optimization greatest practices, you can also make extra knowledgeable selections.

Now that you’ve got seen use the Java and Kotlin programming languages and the Android toolchain in Compiler Explorer, you may degree up your Android improvement expertise.

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

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