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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 low-level programming 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's 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.
In computing, just-in-time (JIT) compilation is 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 compiler optimization, register allocation is the process of assigning local automatic variables and expression results to a limited number of processor registers.
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 which was purchased by and became a division of Oracle Corporation in 2010. Its features improved performance via methods such as just-in-time compilation and adaptive optimization. It is the de facto Java Virtual Machine, serving as the reference implementation of the Java programming language.
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.
In computer programming, a programming language implementation is a system for executing computer programs. There are two general approaches to programming language implementation:
V8 is a JavaScript and WebAssembly engine developed by Google for its Chrome browser. V8 is free and open-source software that is part of the Chromium project and also used separately in non-browser contexts, notably the Node.js runtime system.
In computer programming, profile-guided optimization, also known as profile-directed feedback (PDF) or feedback-directed optimization (FDO), is the compiler optimization technique of using prior analyses of software artifacts or behaviors ("profiling") to improve the expected runtime performance of the program.
In engineering, debugging is the process of finding the root cause, workarounds and possible fixes for bugs.
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.
GraalVM is a Java Development Kit (JDK) written in Java. The open-source distribution of GraalVM is based on OpenJDK, and the enterprise distribution is based on Oracle JDK. As well as just-in-time (JIT) compilation, GraalVM can compile a Java application ahead of time. This allows for faster initialization, greater runtime performance, and decreased resource consumption, but the resulting executable can only run on the platform it was compiled for.
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 Distinguished 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 machine learning. 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", Hacking With Caffeine, 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