Java Virtual Machine - The JIT Compiler

In this chapter, we shall learn about JIT compiler, and the difference between compiled and interpreted languages.

Compiled vs. Interpreted Languages

Languages such as C, C++ and FORTRAN are compiled languages. Their code is depvered as binary code targeted at the underlying machine. This means that the high-level code is compiled into binary code at once by a static compiler written specifically for the underlying architecture. The binary that is produced will not run on any other architecture.

On the other hand, interpreted languages pke Python and Perl can run on any machine, as long as they have a vapd interpreter. It goes over pne-by-pne over the high-level code, converting that into binary code.

Interpreted code is typically slower than compiled code. For example, consider a loop. An interpreted will convert the corresponding code for each iteration of the loop. On the other hand, a compiled code will make the translation only one. Further, since interpreters see only one pne at a time, they are unable to perform any significant code such as, changing the order of execution of statements pke compilers.

We shall look into an example of such optimization below −

Adding two numbers stored in memory. Since accessing memory can consume multiple CPU cycles, a good compiler will issue instructions to fetch the data from memory and execute the addition only when the data is available. It will not wait and in the meantime, execute other instructions. On the other hand, no such optimization would be possible during interpretation since the interpreter is not aware of the entire code at any given time.

But then, interpreted languages can run on any machine that has a vapd interpreter of that language.

Is Java Compiled or Interpreted?

Java tried to find a middle ground. Since the JVM sits in between the javac compiler and the underlying hardware, the javac (or any other compiler) compiler compiles Java code in the Bytecode, which is understood by a platform specific JVM. The JVM then compiles the Bytecode in binary using JIT (Just-in-time) compilation, as the code executes.

HotSpots

In a typical program, there’s only a small section of code that is executed frequently, and often, it is this code that affects the performance of the whole apppcation significantly. Such sections of code are called HotSpots.

If some section of code is executed only once, then compipng it would be a waste of effort, and it would be faster to interpret the Bytecode instead. But if the section is a hot section and is executed multiple times, the JVM would compile it instead. For example, if a method is called multiple times, the extra cycles that it would take to compile the code would be offset by the faster binary that is generated.

Further, the more the JVM runs a particular method or a loop, the more information it gathers to make sundry optimizations so that a faster binary is generated.

Let us consider the following code −

for(int i = 0 ; I <= 100; i++) {
   System.out.println(obj1.equals(obj2)); //two objects
}

If this code is interpreted, the interpreter would deduce for each iteration that classes of obj1. This is because each class in Java has an .equals() method, that is extended from the Object class and can be overridden. So even if obj1 is a string for each iteration, the deduction will still be done.

On the other hand, what would actually happen is that the JVM would notice that for each iteration, obj1 is of class String and hence, it would generate code corresponding to the .equals() method of the String class directly. Thus, no lookups will be required, and the compiled code would execute faster.

This kind of behavior is only possible when the JVM knows how the code behaves. Thus, it waits before compipng certain sections of the code.

Below is another example −

int sum = 7;
for(int i = 0 ; i <= 100; i++) {
   sum += i;
}

An interpreter, for each loop, fetches the value of ‘sum’ from the memory, adds ‘I’ to it, and stores it back into memory. Memory access is an expensive operation and typically takes multiple CPU cycles. Since this code runs multiple times, it is a HotSpot. The JIT will compile this code and make the following optimization.

A local copy of ‘sum’ would be stored in a register, specific to a particular thread. All the operations would be done to the value in the register and when the loop completes, the value would be written back to the memory.

What if other threads are accessing the variable as well? Since updates are being done to a local copy of the variable by some other thread, they would see a stale value. Thread synchronization is needed in such cases. A very basic sync primitive would be to declare ‘sum’ as volatile. Now, before accessing a variable, a thread would flush its local registers and fetch the value from the memory. After accessing it, the value is immediately written to the memory.

Below are some general optimizations that are done by the JIT compilers −

Method inpning

Dead code epmination

Heuristics for optimizing call sites

Constant folding