Introduction to Just in Time Compilers

Just in time compilers are compilers that do compilation during the execution of a program at run time rather than compiling it before execution.JIT compilers are actually a combination of 2 approaches to compilation, Ahead of Time Compilers and Interpreters, and combine the pros and cons of both. Generally, Just In Time compilers consolidates the speed of assembled code with the adaptability of translation, with the overhead of a mediator, and the extra overhead of compiling and linking.

(JIT Compiler in PHP 8)

Major advantages of using Just In Time Compilers:

• Just In Time compilers require less memory.
• Page faults can be minimised.
• While the code is running, it is possible to optimise it using JIT Compiler.
• Different levels of optimisation can be used.

Disadvantages of using JIT Compilers:

• The time it takes to get up and running can be significant.
• Cache memory is used extensively.
• In a Java program, it increases the level of complexity.

Java JIT

One of the most essential components of Java that aids cross-platform execution is bytecode. The manner in which bytecode is converted to native machine code for execution has a significant impact on performance. Depending on the instruction set architecture, the Bytecode must be interpreted/compiled into suitable machine code. Furthermore, if the instruction architecture is bytecode, these can be directly executed. 

To boost performance, JIT compilers interact with the Java Virtual Machine (JVM) at runtime and translate appropriate bytecode sequences into native machine code. In contrast to having the JVM parse the same sequence of bytecode again and incurring costs for the translation process, a JIT compiler allows the hardware to execute native code. As a result, performance levels fluctuate.

While translating a series of bytecode to native machine language, the JIT compiler can perform some simple optimizations. Data analysis, register allocation, translation from stack operations to register operations, and elimination of common sub-expressions are only a few of the optimizations done by JIT compilers.

Because of the additional overhead added to the execution time, it cannot afford to perform all of the optimizations that a static compiler can, and its view of the program is similarly limited.

Java follows the OOP approach, as a result, it consists of classes. These compose of bytecode which is platform-independent and are executed by the Java Virtual Machine across diversified architectures.

• The JVM loads the class files at runtime determine their semantics and performs the relevant computations. When compared to a native application, a Java application performs slowly because of the added CPU and memory requirements during interpretation.
• By translating bytecode into native machine code at run time, the JIT compiler contributes to boosting the performance of Java programs.
• The JIT compiler is enabled all the time, but only when a method is called is it triggered. Instead of interpreting the compiled code for a compiled method, the JVM just calls it. The speed of a native compiler and that of a Java compiler would have been the same if compiling did not involve any CPU time or memory use.
• JIT compilation necessitates the use of both processor time and memory. Thousands of methods are called when the Java virtual machine first starts up. Even if the end result is a very good speed optimization, compiling all of these methods can have a substantial impact on startup time.

Julia: A just in time compiler

Julia is a high performance, high level dynamic programming language. Although it is a general purpose language and can be used to write any form of application, many of its features are well suited for numerical analysis and computational science. A common theme between compiled languages is that they're statically typed. That means when the programmer creates or uses a value, they’re telling the computer what type it is and that information is guaranteed at compile time. Julia is dynamically typed, but internally Julia is much closer to being statically typed. 

Julia code can also compile efficient native code for multiple platforms via the LLVM Backend.