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Introduction
With the release of Android 5.0 Lollipop*, an innovative default runtime environment was introduced, called ART* (short for Android RunTime). It includes a number of enhancements that improve performance. In this paper, we introduce some of the new features in ART, benchmark it against the previous Android Dalvik* runtime, and share five tips for developers that can further improve application performance.
What’s new in ART?
Profiling many Android applications on the Dalvik runtime identified two key pain points for end users: the time it takes to launch an app, and the amount of jank. Jank occurs when an application is stuttering, juddering or simply halting because an app isn't keeping up with the screen refresh rate, and is the result of the frame setup taking too long. A frame is defined as janky when it is much faster or slower than the previous frame. Users see jank as jerky motion which makes the user experience less fluent than users and developers would wish for. To address these issues, there are several new features in ART:
- Ahead of time compilation: At install time, ART compiles apps using the on-device dex2oat tool and generates a compiled app executable for the target device. By comparison, Dalvik used an interpreter and just-in-time compilation, converting an APK into optimized dex byte code at installation time, and further compiling the optimized dex bytecode into native machine code for hot paths when the application is run. The result is that applications launch faster under ART, although the price is that they take longer to install. Applications also use more flash memory space on the device under ART because the code compiled at install time takes up extra space.
- Improved memory allocation: Applications that need to allocate memory intensively might have experienced sluggish performance on Dalvik. A separate large object space and improvements in the memory allocator help to alleviate this.
- Improved garbage collection: ART has faster and more parallel garbage collection, resulting in less fragmentation and better use of memory.
- Improved JNI performance. Optimized JNI invoke and return code sequences reduce the number of instructions used to make JNI calls.
- 64-bit support: ART makes good use of 64 bit architectures, improving the performance of many applications when run on 64-bit hardware.
Together, these features improve the user experience of applications written using the Android SDK alone, as well as applications that make lots of JNI calls. Users may also benefit from longer battery life because applications compile only once and execute faster, and consume less power during routine use as a result.
Comparing performance in ART and Dalvik
When ART was first released as a preview on Android KitKat 4.4, there was some criticism of its performance. That wasn’t a fair comparison because an early preview version of ART was being compared to the fully matured and optimized Dalvik, with the result that some applications ran slower under ART than under Dalvik.
We now have an opportunity to compare the consumer-ready version of ART against Dalvik. Because ART is the only runtime in Android 5.0, a side by side comparison of Dalvik and ART is only possible if you compare devices that have recently been updated from Android KitKat 4.4 to Android Lollipop 5.0. For this paper, we have conducted tests using the TrekStor SurfTab xintron i7.0* tablet with an Intel® AtomTM processor, initially with Android 4.4.4 running Dalvik, and then updated to Android 5.0 running ART.
Since we are comparing different versions of Android it is possible that some of the improvements that we see come from Android 5.0 Improvements other than ART, but based on our internal performance analysis we found that ART is the cause of most of the improvements.
We ran benchmarks where Dalvik’s ability to aggressively optimize code that is repeatedly executed might be expected to give it an advantage, as well as Intel’s own gaming simulation.
Our data shows that ART outperforms Dalvik on the five benchmarks we tested, in some cases significantly.
For more information on these benchmarks, see these links:
IcyRocks version 1.0 is a workload developed by Intel to mimic real-world gaming applications. It uses the open source Cocos2d* library along with JBox2D* (A Java Physics Engine) for most of its computations. It measures the average number of animations (frames) it can render per second (FPS) at various load levels and then computes the final metric by taking a geometric mean of the FPS at these various load levels. It also measures the degree of jank (jank per second), which is the mean of janky frames per second at various load levels. It shows improved performance in ART compared to Dalvik:
IcyRocks version 1.0 also shows that ART renders frames more consistently than Dalvik, with less jank and thus a smoother user experience.
Based on this performance evaluation, it is clear that ART is already delivering a better user experience and better performance than Dalvik.
Moving code from Dalvik to ART
The transition from Dalvik to ART is transparent and most applications that run on Dalvik should run on ART without requiring modification. As a result, many applications will see a performance improvement when users upgrade to the new runtime. It’s still a good idea to test your application with ART, especially if it uses the Java Native Interface, as ART’s JNI error handling is stricter than Dalvik’s, as explained in this article.
Five tips for optimizing your code
Most applications will experience a performance increase as a result of the improvements in ART detailed above. Additionally, there are several practices you can adopt that may help to optimize your application further for ART. For each technique below, we’ve provided some simplified code to illustrate how it works.
Because all applications differ and the resulting performance depends so much on the surrounding code and context, it’s not possible to provide an indication of the performance increase you can expect. However, we will explain why these techniques increase performance, and we recommend that you test them in the context of your own code to see how they affect your performance.
The tips we provide here are broadly applicable, but in the case of ART, the dex2oat compiler that generates binary executable code from a dex file will implement these optimizations.
Tip #1 – Use local variables instead of public class fields when possible
By limiting the scope of variables, you can not only make your code more readable and less error-prone, but also more optimization-friendly.
In the unoptimized code below, the value of v is calculated when the application runs. That’s because v is accessible from outside the method and can be changed by any code, so its value is not known at compilation time. The compiler doesn’t know whether the some_global_call() operation changes v or not, because v can be changed from outside the method by any code.
In the optimized code, v is a local variable and its value can be calculated at compilation time. As a result, the compiler can put the result directly into the code and avoid the calculation at runtime.
Unoptimized code
class A {
public int v = 0;
public int m(){
v = 42;
some_global_call();
return v*3;
}
}
Unoptimized code
class A {
public int m(){
int v = 42;
some_global_call();
return v*3;
}
}
Tip #2 – Use the final keyword to hint that a value is constant
The final keyword can be used to protect your code from accidentally modifying variables that should be constant, but can also improve performance by giving the compiler a hint that a value is constant.
In the unoptimized code below, the value of v*v*v must be calculated at runtime, because the value of v could change. In the optimized code, using the keyword final when assigning a value to v tells the compiler that this value won’t change, so the calculation can be performed during compilation and the result can be added into the code, removing the need to calculate it at runtime.
Unoptimized code
class A {
int v = 42;
public int m(){
return v*v*v;
}
}
Optimized code
class A {
final int v = 42;
public int m(){
return v*v*v;
}
}
Tip #3 – Use the final keyword for class and method definitions
Because all methods in Java are potentially polymorphic, declaring a method or class as final tells the compiler that the method is not redefined in any subclass.
In the unoptimized code below, m() must be resolved before making the call.
In the optimized code, because the method m() was declared as final, the compiler knows which version of m() will be called. As a result, it can avoid method look-up and inline the call, replacing the call to m() with the contents of its method. This results in a performance increase.
Unoptimized code
class A {
public int m(){
return 42;
}
public int f(){
int sum = 0;
for (int i = 0; i < 1000; i++)
sum += m(); return sum;
}
}
Optimized code
class A {
public final int m(){
return 42;
}
public int f(){
int sum = 0;
for (int i = 0; i < 1000; i++)
sum += m();
return sum;
}
}
Tip #4 – Avoid JNI calls for small methods
There are good reasons to use JNI calls, such as when you have a C/C++ codebase or library to reuse, you need a cross-platform implementation, or you need increased performance. But it’s important to minimize the number of JNI calls, because each one carries a significant overhead. When JNI calls are used to optimize performance, this overhead can result in not realizing the expected benefits. In particular, frequently calling short JNI methods can be counter-productive, and putting JNI calls in a loop can amplify the overhead.
Code example
class A {
public final int factorial(int x){
int f = 1;
for (int i =2; i <= x; i++)
f *= i;
return f;
}
public int compute (){
int sum = 0;
for (int i = 0; i < 1000; i++)
sum += factorial(i % 5);
return sum;
}
}
Tip #5 – Use standard libraries instead of implementing the same functionality in your own code
Standard Java libraries are highly optimized and often use internal Java mechanisms to get the best possible performance. They might work significantly faster than when the same functionality is implemented in your own application code. Attempts to avoid the overhead of calling a standard library might actually result in lower performance. In the unoptimized code below, there is custom code to avoid calling Math.abs(). However, the code that uses Math.abs() works faster because Math.abs() is replaced by an optimized internal implementation in ART at compile time.
Unoptimized code
class A {
public static final int abs(int a){
int b;
if (a < 0)
b = a;
else
b = -a;
return b;
}
}
Optimized code
class A {
public static final int abs (int a){
return Math.abs(a);
}
}
Intel optimizations in ART
Intel worked with OEMs to provide an optimized version of Dalvik that provides better performance on Intel processors. Intel is making the same investment in ART, so performance will further increase on the new runtime. Optimizations will be made available through the Android Open Source Project (AOSP), and/or directly through device manufacturers. The optimizations will, as before, be transparent to developers and users so there will be no need to update applications to benefit.
Find out more
To find out more about optimizing your Android applications for Intel processors, and to discover Intel® compilers, visit the Intel Developer Zone at https://software.intel.com.
About the Author
Anil Kumar has been at Intel Corporation for more than 15 years, playing various roles in the Software and Services Group. He is currently Sr. Staff S/W Performance Architect and plays active roles in Java eco-system by contributing to standards organizations, several benchmarks (SPECjbb*, SPECjvm2008, SPECjEnterprise2010 etc.), customer applications by enabling better user experience and resource utilization, and default performance for h/w and s/w configurations.
Daniil Sokolov is a senior software engineer in the Intel Software and Services Group. Daniil has focused on various aspects of Java performance for the last 7 years. He currently works on improving User Experience and Java Application performance on Intel Android devices.
Xavier Hallade is Developer Evangelist at Intel Software and Services Group in Paris, France, where he works on a wide range of Android frameworks, libraries and applications, helping developers to improve their support for new hardware and technologies.
He's also a Google Developer Expert in Android, with a focus on the Android NDK and Android TV.
