The JVM runtime executes < tt >. class </ tt > or < tt >. jar </ tt > files, emulating the JVM instruction set by interpreting it, or using a just-in-time compiler ( JIT ) such as Oracle's HotSpot.

When speed is important, Python programmers tend to try using a JIT compiler such as Psyco or using an alternative language implementation such as PyPy.

One of the significant disadvantages of p-code is execution speed, which can sometimes be remedied through the use of a JIT compiler.

Information gathered at runtime ( ideally with minimal overhead ) can be used by a JIT compiler to dynamically improve optimization.

# Upon execution of a CLI assembly, its code is passed through the runtime's JIT compiler to generate native code.

Source programs are compiled ahead of time and stored as machine independent code, which is then linked at run-time and executed by an interpreter and / or compiler ( for JIT systems ).

A JIT like dynamic compiler was built by Andreas Gal and others, " in which relevant ( i. e., frequently executed ) control flows are ... discovered lazily during execution "

Essentially the JIT compiler can compile small sections of source code statements ( or bytecode ) as they are newly encountered and ( usually ) retain the result for the next time the same source is processed.

Because the JIT processor also has access to run-time information ( that a normal compiler cannot have ) it is also possible for it to optimize further executions depending upon the input and also perform other run-time introspective optimization as execution proceeds.

It is therefore entirely possible that a JIT compiler might ( counter intuitively ) execute even faster than an optimally ' optimized ' compiled program.

TraceMonkey is the first JIT compiler written for the JavaScript language.

JägerMonkey, internally named MethodJIT, is a whole-method JIT compiler designed to improve performance in cases where TraceMonkey cannot generate stable native code.

IonMonkey is the name of Mozilla ’ s future JavaScript JIT compiler, which aims to enable many new optimizations that were impossible with the prior JägerMonkey architecture.

This technique is reminiscent of a JIT compiler, and in fact such compilers ( e. g. Sun's HotSpot technology ) can be viewed as dynamic translators from a virtual instruction set ( the bytecode ) to a real one.

A JIT compiler therefore has to make a trade-off between the compilation time and the quality of the code it hopes to generate.

There also exist Java implementations that combine an AOT ( ahead-of-time ) compiler with either a JIT compiler ( Excelsior JET ) or interpreter ( GNU Compiler for Java.

The earliest published JIT compiler is generally attributed to work on LISP by McCarthy in 1960.

The HP project Dynamo was an experimental JIT compiler where the ' bytecode ' format and the machine code format were the same ; the system turned HPA-8000 machine code into HPA-8000 machine code.

* Sun Microsystems ' Java HotSpot Virtual Machine uses an SSA-based intermediate language in its JIT compiler.

* Mono uses SSA in its JIT compiler called Mini.

* Portable. NET uses SSA in its JIT compiler.

* PyPy uses a linear SSA representation for traces in its JIT compiler.

* Android's Dalvik virtual machine uses SSA in its JIT compiler.

Note that the advent of the mini mill steelmaking facility is starting to challenge how far back JIT can be implemented, as the electric arc furnaces at the heart of many mini-mills can be started and stopped quickly, and steel grades changed rapidly.

The effort to achieve JIT exposes many quality problems that are hidden by buffer stocks ; by forcing smooth flow of only value-adding steps, these problems become visible and must be dealt with explicitly.

This provides many of the advantages of JIT, but the programmer, rather than the runtime, is in control of what parts of the code are compiled.

This can also compile dynamically generated code, which can, in many scenarios, provide substantial performance advantages over statically compiled code, as well as over most JIT systems.

The LLVM JIT compiler can optimize unneeded static branches out of a program at runtime, and thus is useful for partial evaluation in cases where a program has many options, most of which can easily be determined unneeded in a specific environment.

For example JIT can choose SSE2 CPU instructions when it detects that the CPU supports them.

Control flow changes are designed in such a way that the JIT compiler is able to unravel them and produce identical code.

The emulator features a just-in-time ( JIT ) processor emulation core which dynamically translates PPC code into x86 code, caching the results.

Some systems, called dynamic translators, or " just-in-time " ( JIT ) compilers, translate bytecode into machine language as necessary at runtime: this makes the virtual machine unportable, but doesn't lose the portability of the bytecode itself.

Because of this, almost all value chains are split into a part made-to-forecast and a part that could, by using JIT, become make-to-order.

The Server version loads more slowly, putting more effort into producing highly optimized JIT compilations, that yield higher performance.

Like Java, the runtime then provides various callbacks into the agent, for trapping events like method JIT / enter / leave, object creation, etc.

Finally it includes a JIT generator which builds a just-in-time compiler into the interpreter, given a few annotations in the interpreter source code.

Further blurring the distinction between interpreters, byte-code interpreters and compilation is just-in-time compilation ( or JIT ), a technique in which the intermediate representation is compiled to native machine code at runtime.

Several just-in-time compilation ( JIT ) techniques were pioneered and improved in Self research as they were required to allow a very high level object oriented language to perform at up to half the speed of optimized C. Much of the development of Self took place at Sun Microsystems, and the techniques they developed were later deployed for Java's HotSpot virtual machine.

For example, Java and Smalltalk code is typically stored in bytecoded format, which is typically then JIT compiled to translate the bytecode to machine code before execution.

In computing, just-in-time compilation ( JIT ), also known as dynamic translation, is a method to improve the runtime performance of computer programs based on byte code ( virtual machine code ).

In this later case – which is the basis for JIT compilation – the program is stored in memory as byte code, but the code segment currently running is preparatively compiled to physical machine code in order to run faster.

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.

Valgrind is in essence a virtual machine using just-in-time ( JIT ) compilation techniques, including dynamic recompilation.

LLVM allows code to be compiled statically, as it is under the traditional GCC system, or left for late-compiling from the IF to machine code in a just-in-time compiler ( JIT ) fashion similar to Java.

However, according to the man pages supplied with Debian's packages of PearPC, even the JIT core runs around 40 times slower than the host machine would if executing native code.

* Tamarin ( JavaScript engine ) is a free ActionScript and ECMAScript virtual machine and JIT compiler.

Shortly after acquiring Animorphic, Sun also hired Dr. Cliff Click to write a new just-in-time ( JIT ) compiler for the newly developed virtual machine.