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In computing, a compiler is a computer program that translates computer code written in one programming language into another language. The name "compiler" is primarily used for programs that translate source code from a high-level programming language to a lower level language to create an executable program.
In computer science, an interpreter is a computer program that directly executes instructions written in a programming or scripting language, without requiring them previously to have been compiled into a machine language program. An interpreter generally uses one of the following strategies for program execution:
- Parse the source code and perform its behavior directly;
- Translate source code into some efficient intermediate representation or object code and immediately execute that;
- Explicitly execute stored precompiled bytecode made by a compiler and matched with the interpreter Virtual Machine.
In computing, partial evaluation is a technique for several different types of program optimization by specialization. The most straightforward application is to produce new programs that run faster than the originals while being guaranteed to behave in the same way.
Bytecode is a form of instruction set designed for efficient execution by a software interpreter. Unlike human-readable source code, bytecodes are compact numeric codes, constants, and references that encode the result of compiler parsing and performing semantic analysis of things like type, scope, and nesting depths of program objects.
SpiderMonkey is the first JavaScript engine, written by Brendan Eich at Netscape Communications, later released as open source and currently maintained by the Mozilla Foundation. It is used in the Firefox web browser.
In computing, just-in-time (JIT) compilation is a way of executing computer code that involves compilation during execution of a program 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.
In computing, a stack trace is a report of the active stack frames at a certain point in time during the execution of a program. When a program is run, memory is often dynamically allocated in two places; the stack and the heap. Memory is continuously allocated on a stack but not on a heap, thus reflective of their names. Stack also refers to a programming construct, thus to differentiate it, this stack is referred to as the program's function call stack. Technically, once a block of memory has been allocated on the stack, it cannot be easily removed as there can be other blocks of memory that were allocated before it. Each time a function is called in a program, a block of memory called an activation record is allocated on top of the call stack. Generally, the activation record stores the function's arguments and local variables. What exactly it contains and how it's laid out is determined by the calling convention.
HotSpot, released as Java HotSpot Performance Engine, is a Java virtual machine for desktop and server computers, developed by Sun Microsystems and now maintained and distributed by Oracle Corporation. It features improved performance via methods such as just-in-time compilation and adaptive optimization.
In software engineering, profiling is a form of dynamic program analysis that measures, for example, the space (memory) or time complexity of a program, the usage of particular instructions, or the frequency and duration of function calls. Most commonly, profiling information serves to aid program optimization, and more specifically, performance engineering.
PyPy is an implementation of the Python programming language. PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy. Internally, PyPy uses a technique known as meta-tracing, which transforms an interpreter into a tracing just-in-time compiler. Since interpreters are usually easier to write than compilers, but run slower, this technique can make it easier to produce efficient implementations of programming languages. PyPy's meta-tracing toolchain is called RPython.
In computer programming, a programming language implementation is a system for executing computer programs. There are two general approaches to programming language implementation:
Dalvik is a discontinued process virtual machine (VM) in Android operating system that executes applications written for Android. Dalvik was an integral part of the Android software stack in the Android versions 4.4 "KitKat" and earlier, which were commonly used on mobile devices such as mobile phones and tablet computers, and more in some devices such as smart TVs and wearables. Dalvik is open-source software, originally written by Dan Bornstein, who named it after the fishing village of Dalvík in Eyjafjörður, Iceland.
V8 is a free and open-source JavaScript engine developed by the Chromium Project for Google Chrome and Chromium web browsers. The project’s creator is Lars Bak. The first version of the V8 engine was released at the same time as the first version of Chrome: 2 September 2008. It has also been used on the server side, for example in Couchbase and Node.js.
Profile-guided optimization, also known as profile-directed feedback (PDF), and feedback-directed optimization (FDO) is a compiler optimization technique in computer programming that uses profiling to improve program runtime performance.
LuaJIT is a tracing just in time compiler for the Lua programming language.
Tracing just-in-time compilation is a technique used by virtual machines to optimize the execution of a program at runtime. This is done by recording a linear sequence of frequently executed operations, compiling them to native machine code and executing them. This is opposed to traditional just-in-time (JIT) compilers that work on a per-method basis.
Andreas Gal is former chief technology officer at Mozilla. He is most notable for his work on several open source projects and Mozilla technologies.
Michael Franz is an American computer scientist best known for his pioneering work on just-in-time compilation and optimisation and on artificial software diversity. He is a Chancellor's Professor of Computer Science in the Donald Bren School of Information and Computer Sciences at the University of California, Irvine (UCI), a Professor of Electrical Engineering and Computer Science in the Henry Samueli School of Engineering at UCI, and Director of UCI's Secure Systems and Software Laboratory.
Nuitka is a source-to-source compiler which compiles Python code to C source code, applying some compile-time optimizations in the process such as constant folding and propagation, built-in call prediction, type inference, and conditional statement execution. Nuitka initially was designed to produce C++ code, but current versions produce C source code using only those features of C11 that are shared by C++03, enabling further compilation to a binary executable format by modern C and C++ compilers including gcc, clang, MinGW, or Microsoft Visual C++. It accepts Python code compatible with several different Python versions and optionally allows for the creation of standalone programs that do not require Python to be installed on the target computer.
Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher order derivative information. Differentiable programming has found use in a wide variety of areas, particularly scientific computing and artificial intelligence. One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.
References
- Bala, Vasanth; Duesterwald, Evelyn; Banerjia, Sanjeev (June 1999), Transparent Dynamic Optimization: The Design and Implementation of Dynamo , retrieved 2020-12-18
- Gal, Andreas; Franz, Michael (November 2006), Incremental Dynamic Code Generation with Trace Trees (PDF), CiteSeerX 10.1.1.113.557 , S2CID 9352256 , retrieved 2020-12-18
- Gal, Andreas; Bebenita, Michael; Chang, Mason; Franz, Michael (October 2007), Making the Compilation "Pipeline" Explicit: Dynamic Compilation Using Trace Tree Serialization, CiteSeerX 10.1.1.85.2412 , S2CID 14848180
- Double, Chris (February 2008), Quick Introduction to Tamarin Tracing , retrieved 2020-12-18
- Chang, Mason (January 12, 2009), The Difference Between Extended Basic Blocks and Traces, archived from the original on 2020-01-29
- Bolz, Carl Friedrich (March 2, 2009), PyPy Blog: Applying a Tracing JIT to an Interpreter , retrieved 2020-12-18