Just-in-time (JIT) compilation (also dynamic translation or run-time compilations) is compilation of computer code during execution of a program at run time rather than before execution. This may consist of source code translation but is more commonly bytecode translation to machine code, which is then executed directly. A system implementing a JIT compiler typically continuously analyses the code being executed and identifies parts of the code where the speedup gained from compilation or recompilation would outweigh the overhead of compiling that code. JIT compilation is a combination of the two traditional approaches to translation to machine code: ahead-of-time compilation (AOT), and interpretation, which combines some advantages and drawbacks of both. Roughly, JIT compilation combines the speed of compiled code with the flexibility of interpretation, with the overhead of an interpreter and the additional overhead of compiling and linking (not just interpreting). JIT compilation is a form of dynamic compilation, and allows adaptive optimization such as dynamic recompilation and microarchitecture-specific speedups. Interpretation and JIT compilation are particularly suited for dynamic programming languages, as the runtime system can handle late-bound data types and enforce security guarantees.
History The earliest published JIT compiler is generally attributed to work on LISP by John McCarthy in 1960. In his seminal paper Recursive functions of symbolic expressions and their computation by machine, Part I, he mentions functions that are translated during runtime, thereby sparing the need to save the compiler output to punch cards (although this would be more accurately known as a "Compile and go system"). Another early example was by Ken Thompson, who in 1968 gave one of the first applications of regular expressions, here for pattern matching in the text editor QED. For speed, Thompson implemented regular expression matching by JITing to IBM 7094 code on the Compatible Time-Sharing System. An influential technique for deriving compiled code from interpretation was pioneered by James G. Mitchell in 1970, which he implemented for the experimental language LC². Smalltalk (c. 1980) pioneered new aspects of JIT compilations. For example, translation to machine code was done on demand, and the result was cached for later use. When memory became scarce, the system would delete some of this code and regenerate it when it was needed again. Sun's Self language improved these techniques extensively and was at one point the fastest Smalltalk system in the world, achieving up to half the speed of optimized C but with a fully object-oriented programming language. Self was abandoned by Sun, but the research went into the Java language. The term "Just-in-time compilation" was borrowed from the manufacturing term "Just in time" and popularized by Java, with James Gosling using the term from 1993. Currently JITing is used by most implementations of the Java virtual machine, as HotSpot builds on, and extensively uses, this research base. The HP project Dynamo was an experimental JIT compiler where the "bytecode" format and the machine code format were the same; the system optimized PA-8000 machine code. Counterintuitively, this resulted in speed ups, in some cases of 30% since doing this permitted optimizations at the machine code level, for example, inlining code for better cache usage and optimizations of calls to dynamic libraries and many other run-time optimizations which conventional compilers are not able to attempt. In November 2020, PHP 8.0 introduced a JIT compiler. In October 2024, CPython introduced an experimental JIT compiler.
Design In a bytecode-compiled system, source code is translated to an intermediate representation known as bytecode. Bytecode is not the machine code for any particular computer, and may be portable among computer architectures. The bytecode may then be interpreted by, or run on a virtual machine. The JIT compiler reads the bytecodes in many sections (or in full, rarely) and compiles them dynamically into machine code so the program can run faster. This can be done per-file, per-function or even on any arbitrary code fragment; the code can be compiled when it is about to be executed (hence the name "just-in-time"), and then cached and reused later without needing to be recompiled. By contrast, a traditional interpreted virtual machine will simply interpret the bytecode, generally with much lower performance. Some interpreters even interpret source code, without the step of first compiling to bytecode, with even worse performance. Statically-compiled code or native code is compiled prior to deployment. A dynamic compilation environment is one in which the compiler can be used during execution. A common goal of using JIT techniques is to reach or surpass the performance of static compilation, while maintaining the advantages of bytecode interpretation: Much of the "heavy lifting" of parsing the original source code and performing basic optimization is often handled at compile time, prior to deployment: compilation from bytecode to machine code is much faster than compiling from source. The deployed bytecode is portable, unlike native code. Since the runtime has control over the compilation, like interpreted bytecode, it can run in a secure sandbox. Compilers from bytecode to machine code are easier to write, because the portable bytecode compiler has already done much of the work. JIT code generally offers far better performance than interpreters. In addition, it can in some cases offer better performance than static compilation, as many optimizations are only feasible at run-time:
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