In java/android we can call code written in the c/c++ language for execution speed advantage. I have heard of Ahead Of Time compilation which (as far as i know) compiles the entire application to native code during its installation.

I want to perform some mathematical operations like compression, encryption (not images, audio or video processing) to compare computational power of devices and whose code can be written in both java as well as c/c++.

My doubt is: since all the java code is converted to native in android is there any use of JNI/NDK and what is it?

Update1: Here it is mentioned:

Unlike Dalvik, ART introduces the use of ahead-of-time (AOT) compilation by compiling entire applications into native machine code upon their installation.

Consider i have an encryption algorithm (which does not use any language specific constructs like pointers). Which normally when compiled to native code (c/cpp) will give maximum execution speed. But as quoted, if all the code is compiled into native upon installation of app is there any difference of performance(speed) between the encryption method(written in java) and (same)encryption funtion (in cpp).

I want to know best approach for implementing encryption algorithm in android or similar vm based os.

  • I am not sure that ART is JNI compatible. You need to check that. And in some cases, C++ code can be slower than Java (even if that is not common). – Basile Starynkevitch Nov 5 '17 at 8:19

Assume it is not just an Android application. Assume your company needs applications for Android, iOS, Windows, MacOS and Linux. And there is shared functionality. Which is written in a language available on all platforms, that is C++.

You now have the choice of re-writing a huge part of your application in Java (and Objective-C or Swift, and C#, and whatever is the fashion in Linux), which will be very expensive and a maintenance nightmare, or using the C++ code using JNI.

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  • please see update – pebble Nov 6 '17 at 8:56
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    @pebble How does your update affect this answer? Java is still not very useful for iOS or Windows Phone. – Sebastian Redl Nov 6 '17 at 9:04
  • @redl by java i mean android. I want to know in android should I use JNI/NDK to implement encryption or not – pebble Nov 6 '17 at 14:36

There are a few reasons why JNI is useful:

  • Use already built, or share C++ libraries
  • Generally it is believed that C++ can offer better performance than what the Java bytecode code will be interpreted to. With C++ you have finer control over memory usage and can be more deliberate to achieve high performance.
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  • please refer updated question – pebble Nov 6 '17 at 8:56
  • My point still stands. Just because an application is compiled into machine code doesn't mean it is the most efficient machine code. If you write in C++, you can fine tune the machine code that's generated more easily. – Samuel Nov 6 '17 at 18:55

My doubt is: since all the java code is converted to native in android is there any use of JNI/NDK and what is it?

Control over memory is the biggest one for me. I've seen Java do a very competent job of register allocation and instruction selection but there's one glaring difficulty for performance, and that's the overhead associated with objects and the loss that gives you over memory layout and access patterns. Still, you can make Java code a lot more performant than usual and gain back a lot of the control lost if you can lean more heavily on plain old data types and not objects.

For example, in C you can do things like this:

struct Vector
    // AoS
    float xyzw[4];

And create an array of those and be guaranteed that the contents will be contiguous with a stride that's always sizeof(struct Vector) which will generally exclude padding in this case and just be sizeof(float)*4 or exactly 128 bits. You can also heap allocate that aligned to 128-bit boundaries and then be able to use aligned moves to SIMD registers and vectorize your code with efficient intrinsics. Similar thing in C++ where you can make a vector class with methods and not pay any overhead for the convenience and still guarantee that an array of those will be contiguous.

However, if you try to create a Vector class in Java, then you gain no such control, each Vector object will have some additional meta information associated for things like reflection and dynamic dispatch which will inflate the size of a Vector. If you try to create an array of those, it'll be similar to creating an array of pointers at which point you pay additional memory and indirection costs of the pointers on top of the class metadata for each Vector instance, and the contents of the array won't be guaranteed to be contiguous (they likely will be initially if you allocate each individual Vector sequentially all at once, but after the first GC cycle, they can then be fragmented in memory). That can be a performance killer with the loss of temporal and especially spatial locality, leading to many more cache misses than necessary.

That said, often there's plenty of room for Java code to go a whole lot faster. If you just work with a giant array of float, for example, instead of an array of Vector, you avoid all the overhead above and can be guaranteed that the contents of the array will remain contiguous. I actually think many people stand to make their Java applications a whole lot performant without reaching for the JNI if they could just work with arrays of plain old data types, not objects, for the areas they're tempted to implement in JNI. For convenience you can make a Vectors class which stores a bunch of vectors at once (the object overhead will then be trivial if it's only paid once for a hundred vectors, e.g., and not per-vector) and provides operations on them, but internally just represents those vectors as a big array of float.

I've even seen a reasonable interactive CPU path tracer implemented in Java (no native API calls for the path tracing itself, including the BVH). It was surprisingly fast, especially for a teeny one-man amateur project, but when I peered at the source code, that's exactly what it did. It avoided objects for performance-critical hot data in favor of giant arrays of plain old data types. It avoided even using Vector and Matrix objects, instead just using arrays of floats and index ranges. Of course the implementation wasn't so pretty and looked a lot like old unstructured C code, but that was only for the low-level critical paths and data that was accessed billions of times over. The high-level part of the application was still modeled with objects. Their implementation details, however, avoided them.

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