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78-408: PyPy itself is built using a technique known as meta-tracing, which is a mostly automatic transformation that takes an interpreter as input and produces a tracing just-in-time compiler as output. 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

156-484: A garbage collector and debugger . Programs written in a high-level language are either directly executed by some kind of interpreter or converted into machine code by a compiler (and assembler and linker ) for the CPU to execute. While compilers (and assemblers) generally produce machine code directly executable by computer hardware, they can often (optionally) produce an intermediate form called object code . This

234-468: A variable-length code requiring 3, 6, 10, or 18 bits, and address operands include a "bit offset". Many BASIC interpreters can store and read back their own tokenized internal representation. An interpreter might well use the same lexical analyzer and parser as the compiler and then interpret the resulting abstract syntax tree . Example data type definitions for the latter, and a toy interpreter for syntax trees obtained from C expressions are shown in

312-466: A virtual machine , which is implemented not in hardware, but in the bytecode interpreter. Such compiling interpreters are sometimes also called compreters . In a bytecode interpreter each instruction starts with a byte, and therefore bytecode interpreters have up to 256 instructions, although not all may be used. Some bytecodes may take multiple bytes, and may be arbitrarily complicated. Control tables - that do not necessarily ever need to pass through

390-637: A $ 10,000 grant for PyPy to continue work on performance and compatibility with newer versions of the language. The port to ARM architecture was sponsored in part by the Raspberry Pi Foundation . The PyPy project also accepts donations through its status blog pages. As of 2013, a variety of sub-projects had funding: Python 3 version compatibility, built-in optimized NumPy support for numerical calculations and software transactional memory support to allow better parallelism. Interpreter (computing) In computer science , an interpreter

468-441: A bytecode interpreter, because of nodes related to syntax performing no useful work, of a less sequential representation (requiring traversal of more pointers) and of overhead visiting the tree. Further blurring the distinction between interpreters, bytecode interpreters and compilation is just-in-time (JIT) compilation, a technique in which the intermediate representation is compiled to native machine code at runtime. This confers

546-492: A clean separation between language specification and implementation aspects. It also aims to provide a compliant, flexible and fast implementation of the Python programming language using the above framework to enable new advanced features without having to encode low-level details into it. The PyPy interpreter itself is written in a restricted subset of Python called RPython (Restricted Python). RPython puts some constraints on

624-413: A compiler works. However, a compiled program still runs much faster, under most circumstances, in part because compilers are designed to optimize code, and may be given ample time for this. This is especially true for simpler high-level languages without (many) dynamic data structures, checks, or type checking . In traditional compilation, the executable output of the linkers (.exe files or .dll files or

702-439: A compiling phase - dictate appropriate algorithmic control flow via customized interpreters in similar fashion to bytecode interpreters. Threaded code interpreters are similar to bytecode interpreters but instead of bytes they use pointers. Each "instruction" is a word that points to a function or an instruction sequence, possibly followed by a parameter. The threaded code interpreter either loops fetching instructions and calling

780-413: A computer language is usually done in relation to an abstract machine (so-called operational semantics ) or as a mathematical function ( denotational semantics ). A language may also be defined by an interpreter in which the semantics of the host language is given. The definition of a language by a self-interpreter is not well-founded (it cannot define a language), but a self-interpreter tells a reader about

858-418: A library, see picture) is typically relocatable when run under a general operating system, much like the object code modules are but with the difference that this relocation is done dynamically at run time, i.e. when the program is loaded for execution. On the other hand, compiled and linked programs for small embedded systems are typically statically allocated, often hard coded in a NOR flash memory, as there

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936-714: A list of these commands in the order a programmer wishes to execute them. Each command (also known as an Instruction ) contains the data the programmer wants to mutate, and information on how to mutate the data. For example, an interpreter might read ADD Books, 5 and interpret it as a request to add five to the Books variable . Interpreters have a wide variety of instructions which are specialized to perform different tasks, but you will commonly find interpreter instructions for basic mathematical operations , branching , and memory management , making most interpreters Turing complete . Many interpreters are also closely integrated with

1014-439: A parse tree, and both may generate immediate instructions (for a stack machine , quadruple code , or by other means). The basic difference is that a compiler system, including a (built in or separate) linker, generates a stand-alone machine code program, while an interpreter system instead performs the actions described by the high-level program. A compiler can thus make almost all the conversions from source code semantics to

1092-405: A similar effect to obfuscation, but bytecode could be decoded with a decompiler or disassembler . The main disadvantage of interpreters is that an interpreted program typically runs more slowly than if it had been compiled . The difference in speeds could be tiny or great; often an order of magnitude and sometimes more. It generally takes longer to run a program under an interpreter than to run

1170-401: A suffix of the form _ n , where n is a number from 0–3 for load and store . The maximum n for const differs by type. The const instructions push a value of the specified type onto the stack. For example, iconst_5 will push an integer (32 bit value) with the value 5 onto the stack, while dconst_1 will push a double (64 bit floating point value) with the value 1 onto

1248-494: A suitable interpreter. If the interpreter needs to be supplied along with the source, the overall installation process is more complex than delivery of a monolithic executable, since the interpreter itself is part of what needs to be installed. The fact that interpreted code can easily be read and copied by humans can be of concern from the point of view of copyright . However, various systems of encryption and obfuscation exist. Delivery of intermediate code, such as bytecode, has

1326-472: A template interpreter. Rather than implement the execution of code by virtue of a large switch statement containing every possible bytecode, while operating on a software stack or a tree walk, a template interpreter maintains a large array of bytecode (or any efficient intermediate representation) mapped directly to corresponding native machine instructions that can be executed on the host hardware as key value pairs (or in more efficient designs, direct addresses to

1404-415: A wide range of computational tasks, including binary emulation and internet applications. Interpreter performance is still a worry despite their adaptability, particularly on systems with limited hardware resources. Advanced instrumentation and tracing approaches provide insights into interpreter implementations and processor resource utilization during execution through evaluations of interpreters tailored for

1482-412: 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: Early versions of Lisp programming language and minicomputer and microcomputer BASIC dialects would be examples of

1560-483: Is a snake swallowing itself since the RPython is translated by a Python interpreter . The code can also be run untranslated for testing and analysis, which provides a nice test-bed for research into dynamic languages. It allows for pluggable garbage collectors , as well as optionally enabling Stackless Python features. Finally, it includes a just-in-time (JIT) generator that builds a just-in-time compiler into

1638-432: Is a few decades old, appearing in languages such as Smalltalk in the 1980s. Just-in-time compilation has gained mainstream attention amongst language implementers in recent years, with Java , the .NET Framework , most modern JavaScript implementations, and Matlab now including JIT compilers. Making the distinction between compilers and interpreters yet again even more vague is a special interpreter design known as

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1716-688: Is a layer of hardware-level instructions that implement higher-level machine code instructions or internal state machine sequencing in many digital processing elements. Microcode is used in general-purpose central processing units , as well as in more specialized processors such as microcontrollers , digital signal processors , channel controllers , disk controllers , network interface controllers , network processors , graphics processing units , and in other hardware. Microcode typically resides in special high-speed memory and translates machine instructions, state machine data or other input into sequences of detailed circuit-level operations. It separates

1794-536: Is a relatively simple way to achieve software compatibility between different products in a processor family. Even a non microcoding computer processor itself can be considered to be a parsing immediate execution interpreter that is written in a general purpose hardware description language such as VHDL to create a system that parses the machine code instructions and immediately executes them. Interpreters, such as those written in Java, Perl, and Tcl, are now necessary for

1872-454: Is also known as PyPy3. PyPy has JIT compilation support on 32-bit/64-bit x86 and 32-bit/64-bit ARM processors. It is tested nightly on Windows, Linux, OpenBSD and Mac OS X. PyPy is able to run pure Python software that does not rely on implementation-specific features. There is a compatibility layer for CPython C API extensions called CPyExt, but it is incomplete and experimental. The preferred way of interfacing with C shared libraries

1950-583: Is basically the same machine specific code but augmented with a symbol table with names and tags to make executable blocks (or modules) identifiable and relocatable. Compiled programs will typically use building blocks (functions) kept in a library of such object code modules. A linker is used to combine (pre-made) library files with the object file(s) of the application to form a single executable file. The object files that are used to generate an executable file are thus often produced at different times, and sometimes even by different languages (capable of generating

2028-434: Is both a stack machine and a register machine . Each frame for a method call has an "operand stack" and an array of "local variables". The operand stack is used for operands to computations and for receiving the return value of a called method, while local variables serve the same purpose as registers and are also used to pass method arguments. The maximum size of the operand stack and local variable array, computed by

2106-504: Is called RPython . PyPy does not have full compatibility with more recent versions of the CPython ecosystem. While it claims compatibility with Python 2.7, 3.7, 3.8 and 3.9 ("a drop-in replacement for CPython"), it lacks some of the newer features and syntax in Python 3.10, such as syntax for pattern matching . PyPy aims to provide a common translation and support framework for producing implementations of dynamic languages , emphasizing

2184-439: Is compiled into "F code" (a bytecode), which is then interpreted by a virtual machine . In the spectrum between interpreting and compiling, another approach is to transform the source code into an optimized abstract syntax tree (AST), then execute the program following this tree structure, or use it to generate native code just-in-time . In this approach, each sentence needs to be parsed just once. As an advantage over bytecode,

2262-440: Is composed of one byte that represents the opcode , along with zero or more bytes for operands. Of the 256 possible byte-long opcodes , as of 2015 , 202 are in use (~79%), 51 are reserved for future use (~20%), and 3 instructions (~1%) are permanently reserved for JVM implementations to use. Two of these ( impdep1 and impdep2 ) are to provide traps for implementation-specific software and hardware, respectively. The third

2340-432: Is executed and then perform the desired action, whereas the compiled code just performs the action within a fixed context determined by the compilation. This run-time analysis is known as "interpretive overhead". Access to variables is also slower in an interpreter because the mapping of identifiers to storage locations must be done repeatedly at run-time rather than at compile time . There are various compromises between

2418-430: Is implemented using closures in the interpreter language or implemented "manually" with a data structure explicitly storing the environment. The more features implemented by the same feature in the host language, the less control the programmer of the interpreter has; for example, a different behavior for dealing with number overflows cannot be realized if the arithmetic operations are delegated to corresponding operations in

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2496-565: Is more difficult to maintain due to the interpreter having to support translation to multiple different architectures instead of a platform independent virtual machine/stack. To date, the only template interpreter implementations of widely known languages to exist are the interpreter within Java's official reference implementation, the Sun HotSpot Java Virtual Machine, and the Ignition Interpreter in

2574-449: Is often no secondary storage and no operating system in this sense. Historically, most interpreter systems have had a self-contained editor built in. This is becoming more common also for compilers (then often called an IDE ), although some programmers prefer to use an editor of their choice and run the compiler, linker and other tools manually. Historically, compilers predate interpreters because hardware at that time could not support both

2652-490: Is such a language, because XSLT programs are written in XML. A sub-domain of metaprogramming is the writing of domain-specific languages (DSLs). Clive Gifford introduced a measure quality of self-interpreter (the eigenratio), the limit of the ratio between computer time spent running a stack of N self-interpreters and time spent to run a stack of N − 1 self-interpreters as N goes to infinity. This value does not depend on

2730-494: Is the instruction set of the Java virtual machine (JVM), the language to which Java and other JVM-compatible source code is compiled . Each instruction is represented by a single byte , hence the name bytecode , making it a compact form of data . Due to the nature of bytecode, a Java bytecode program is runnable on any machine with a compatible JVM; without the lengthy process of compiling from source code. Java bytecode

2808-593: Is through the built-in C foreign function interface (CFFI) or ctypes libraries. PyPy is a followup to the Psyco project, a just-in-time specializing compiler for Python, developed by Armin Rigo between 2002 and 2010. PyPy's aim is to have a just-in-time specializing compiler with scope, which was not available for Psyco. Initially, the RPython could also be compiled into Java bytecode , CIL and JavaScript , but these backends were removed due to lack of interest. PyPy

2886-466: Is used at runtime either interpreted by a JVM or compiled to machine code via just-in-time (JIT) compilation and run as a native application. As Java bytecode is designed for a cross-platform compatibility and security, a Java bytecode application tends to run consistently across various hardware and software configurations. In general, a Java programmer does not need to understand Java bytecode or even be aware of it. However, as suggested in

2964-499: Is used for debuggers to implement breakpoints. Instructions fall into a number of broad groups: There are also a few instructions for a number of more specialized tasks such as exception throwing, synchronization, etc. Many instructions have prefixes and/or suffixes referring to the types of operands they operate on. These are as follows: For example, iadd will add two integers, while dadd will add two doubles. The const , load , and store instructions may also take

3042-577: The Google Open Source programs and has agreed to focus on making PyPy more compatible with CPython . In 2009 Eurostars, a European Union funding agency specially focused on SMEs , accepted a proposal from PyPy project members titled "PYJIT – a fast and flexible toolkit for dynamic programming languages based on PyPy". Eurostars funding lasted until August 2011. At PyCon US 2011, the Python Software Foundation provided

3120-503: The IBM developerWorks journal, "Understanding bytecode and what bytecode is likely to be generated by a Java compiler helps the Java programmer in the same way that knowledge of assembly helps the C or C++ programmer." The bytecode comprises various instruction types, including data manipulation, control transfer, object creation and manipulation, and method invocation, all integral to Java's object-oriented programming model. The JVM

3198-427: The development speed when using an interpreter and the execution speed when using a compiler. Some systems (such as some Lisps ) allow interpreted and compiled code to call each other and to share variables. This means that once a routine has been tested and debugged under the interpreter it can be compiled and thus benefit from faster execution while other routines are being developed. Many interpreters do not execute

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3276-471: The AST keeps the global program structure and relations between statements (which is lost in a bytecode representation), and when compressed provides a more compact representation. Thus, using AST has been proposed as a better intermediate format for just-in-time compilers than bytecode. Also, it allows the system to perform better analysis during runtime. However, for interpreters, an AST causes more overhead than

3354-508: The Google V8 javascript execution engine. A self-interpreter is a programming language interpreter written in a programming language which can interpret itself; an example is a BASIC interpreter written in BASIC. Self-interpreters are related to self-hosting compilers . If no compiler exists for the language to be interpreted, creating a self-interpreter requires the implementation of

3432-610: The Java virtual machine, such as: There are several Java virtual machines available today to execute Java bytecode, both free and commercial products. If executing bytecode in a virtual machine is undesirable, a developer can also compile Java source code or bytecode directly to native machine code with tools such as the GNU Compiler for Java (GCJ). Some processors can execute Java bytecode natively. Such processors are termed Java processors . The Java virtual machine provides some support for dynamically typed languages . Most of

3510-676: The Lisp eval function could be implemented in machine code. The result was a working Lisp interpreter which could be used to run Lisp programs, or more properly, "evaluate Lisp expressions". The development of editing interpreters was influenced by the need for interactive computing. In the 1960s, the introduction of time-sharing systems allowed multiple users to access a computer simultaneously, and editing interpreters became essential for managing and modifying code in real-time. The first editing interpreters were likely developed for mainframe computers, where they were used to create and modify programs on

3588-474: The MIPS instruction set and programming languages such as Tcl, Perl, and Java. Performance characteristics are influenced by interpreter complexity, as demonstrated by comparisons with compiled code. It is clear that interpreter performance is more dependent on the nuances and resource needs of the interpreter than it is on the particular application that is being interpreted. Java bytecode Java bytecode

3666-411: The Python language such that a variable's type can be inferred at compile time. The PyPy project has developed a toolchain that analyzes RPython code and translates it into a form of byte code , which can be lowered into C . There used to be other backends in addition to C ( Java , C# , and JavaScript ), but those suffered from bitrot and have been removed. Thus, the recursive logo of PyPy

3744-415: The amount of analysis performed before the program is executed. For example, Emacs Lisp is compiled to bytecode , which is a highly compressed and optimized representation of the Lisp source, but is not machine code (and therefore not tied to any particular hardware). This "compiled" code is then interpreted by a bytecode interpreter (itself written in C ). The compiled code in this case is machine code for

3822-464: The box. Interpretation cannot be used as the sole method of execution: even though an interpreter can itself be interpreted and so on, a directly executed program is needed somewhere at the bottom of the stack because the code being interpreted is not, by definition, the same as the machine code that the CPU can execute. There is a spectrum of possibilities between interpreting and compiling, depending on

3900-418: The compiled code but it can take less time to interpret it than the total time required to compile and run it. This is especially important when prototyping and testing code when an edit-interpret-debug cycle can often be much shorter than an edit-compile-run-debug cycle. Interpreting code is slower than running the compiled code because the interpreter must analyze each statement in the program each time it

3978-411: The compiler, is part of the attributes of each method. Each can be independently sized from 0 to 65535 values, where each value is 32 bits. long and double types, which are 64 bits, take up two consecutive local variables (which need not be 64-bit aligned in the local variables array) or one value in the operand stack (but are counted as two units in the depth of the stack). Each bytecode

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4056-436: The efficiency of running native code, at the cost of startup time and increased memory use when the bytecode or AST is first compiled. The earliest published JIT compiler is generally attributed to work on LISP by John McCarthy in 1960. Adaptive optimization is a complementary technique in which the interpreter profiles the running program and compiles its most frequently executed parts into native code. The latter technique

4134-414: The expressiveness and elegance of a language. It also enables the interpreter to interpret its source code, the first step towards reflective interpreting. An important design dimension in the implementation of a self-interpreter is whether a feature of the interpreted language is implemented with the same feature in the interpreter's host language. An example is whether a closure in a Lisp -like language

4212-582: The extant JVM instruction set is statically typed - in the sense that method calls have their signatures type-checked at compile time , without a mechanism to defer this decision to run time , or to choose the method dispatch by an alternative approach. JSR 292 ( Supporting Dynamically Typed Languages on the Java Platform ) added a new invokedynamic instruction at the JVM level, to allow method invocation relying on dynamic type checking (instead of

4290-665: The first type. Perl , Raku , Python , MATLAB , and Ruby are examples of the second, while UCSD Pascal is an example of the third type. 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). Some systems, such as Smalltalk and contemporary versions of BASIC and Java , may also combine two and three types. Interpreters of various types have also been constructed for many languages traditionally associated with compilation, such as Algol , Fortran , Cobol , C and C++ . While interpretation and compilation are

4368-587: The fly. One of the earliest examples of an editing interpreter is the EDT (Editor and Debugger for the TECO) system, which was developed in the late 1960s for the PDP-1 computer. EDT allowed users to edit and debug programs using a combination of commands and macros, paving the way for modern text editors and interactive development environments. An interpreter usually consists of a set of known commands it can execute , and

4446-515: The functions they point to, or fetches the first instruction and jumps to it, and every instruction sequence ends with a fetch and jump to the next instruction. Unlike bytecode there is no effective limit on the number of different instructions other than available memory and address space. The classic example of threaded code is the Forth code used in Open Firmware systems: the source language

4524-479: The host language. Some languages such as Lisp and Prolog have elegant self-interpreters. Much research on self-interpreters (particularly reflective interpreters) has been conducted in the Scheme programming language , a dialect of Lisp. In general, however, any Turing-complete language allows writing of its own interpreter. Lisp is such a language, because Lisp programs are lists of symbols and other lists. XSLT

4602-414: The interpreter and interpreted code and the typical batch environment of the time limited the advantages of interpretation. During the software development cycle , programmers make frequent changes to source code. When using a compiler, each time a change is made to the source code, they must wait for the compiler to translate the altered source files and link all of the binary code files together before

4680-419: The interpreter, given a few annotations in the interpreter source code . The generated JIT compiler is a tracing JIT . RPython is now also used to write non-Python language implementations, such as Pixie . PyPy as of version 7.3.17 is compatible with two CPython versions: 2.7 and 3.10. The first PyPy version compatible with CPython v3 is PyPy v2.3.1 (2014). The PyPy interpreter compatible with CPython v3

4758-453: The language in a host language (which may be another programming language or assembler ). By having a first interpreter such as this, the system is bootstrapped and new versions of the interpreter can be developed in the language itself. It was in this way that Donald Knuth developed the TANGLE interpreter for the language WEB of the de-facto standard TeX typesetting system . Defining

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4836-405: The language into native calls one opcode at a time rather than creating optimized sequences of CPU executable instructions from the entire code segment. Due to the interpreter's simple design of simply passing calls directly to the hardware rather than implementing them directly, it is much faster than every other type, even bytecode interpreters, and to an extent less prone to bugs, but as a tradeoff

4914-633: The limitations of computers at the time (e.g. a shortage of program storage space, or no native support for floating point numbers). Interpreters were also used to translate between low-level machine languages, allowing code to be written for machines that were still under construction and tested on computers that already existed. The first interpreted high-level language was Lisp . Lisp was first implemented by Steve Russell on an IBM 704 computer. Russell had read John McCarthy 's paper, "Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I", and realized (to McCarthy's surprise) that

4992-572: The machine instructions from the underlying electronics so that instructions can be designed and altered more freely. It also facilitates the building of complex multi-step instructions, while reducing the complexity of computer circuits. Writing microcode is often called microprogramming and the microcode in a particular processor implementation is sometimes called a microprogram . More extensive microcoding allows small and simple microarchitectures to emulate more powerful architectures with wider word length , more execution units and so on, which

5070-421: The machine level once and for all (i.e. until the program has to be changed) while an interpreter has to do some of this conversion work every time a statement or function is executed. However, in an efficient interpreter, much of the translation work (including analysis of types, and similar) is factored out and done only the first time a program, module, function, or even statement, is run, thus quite akin to how

5148-430: The native instructions), known as a "Template". When the particular code segment is executed the interpreter simply loads or jumps to the opcode mapping in the template and directly runs it on the hardware. Due to its design, the template interpreter very strongly resembles a just-in-time compiler rather than a traditional interpreter, however it is technically not a JIT due to the fact that it merely translates code from

5226-410: The program being run. The book Structure and Interpretation of Computer Programs presents examples of meta-circular interpretation for Scheme and its dialects. Other examples of languages with a self-interpreter are Forth and Pascal . Microcode is a very commonly used technique "that imposes an interpreter between the hardware and the architectural level of a computer". As such, the microcode

5304-411: The program can be executed. The larger the program, the longer the wait. By contrast, a programmer using an interpreter does a lot less waiting, as the interpreter usually just needs to translate the code being worked on to an intermediate representation (or not translate it at all), thus requiring much less time before the changes can be tested. Effects are evident upon saving the source code and reloading

5382-458: The program. Compiled code is generally less readily debugged as editing, compiling, and linking are sequential processes that have to be conducted in the proper sequence with a proper set of commands. For this reason, many compilers also have an executive aid, known as a Makefile and program. The Makefile lists compiler and linker command lines and program source code files, but might take a simple command line menu input (e.g. "Make 3") which selects

5460-619: The same object format). A simple interpreter written in a low-level language (e.g. assembly ) may have similar machine code blocks implementing functions of the high-level language stored, and executed when a function's entry in a look up table points to that code. However, an interpreter written in a high-level language typically uses another approach, such as generating and then walking a parse tree , or by generating and executing intermediate software-defined instructions, or both. Thus, both compilers and interpreters generally turn source code (text files) into tokens, both may (or may not) generate

5538-537: The source code as it stands but convert it into some more compact internal form. Many BASIC interpreters replace keywords with single byte tokens which can be used to find the instruction in a jump table . A few interpreters, such as the PBASIC interpreter, achieve even higher levels of program compaction by using a bit-oriented rather than a byte-oriented program memory structure, where commands tokens occupy perhaps 5 bits, nominally "16-bit" constants are stored in

5616-464: The specifications for Java bytecode are now available, other parties have supplied compilers that produce Java bytecode. Examples of other compilers include: Some projects provide Java assemblers to enable writing Java bytecode by hand. Assembly code may be also generated by machine, for example by a compiler targeting a Java virtual machine . Notable Java assemblers include: Others have developed compilers, for different programming languages, to target

5694-436: The stack. There is also an aconst_null , which pushes a null reference. The n for the load and store instructions specifies the index in the local variable array to load from or store to. The aload_0 instruction pushes the object in local variable 0 onto the stack (this is usually the this object). istore_1 stores the integer on the top of the stack into local variable 1. For local variables beyond 3

5772-452: The suffix is dropped and operands must be used. Consider the following Java code: A Java compiler might translate the Java code above into bytecode as follows, assuming the above was put in a method: The most common language targeting Java virtual machine by producing Java bytecode is Java. Originally only one compiler existed, the javac compiler from Sun Microsystems , which compiles Java source code to Java bytecode; but because all

5850-506: The third group (set) of instructions then issues the commands to the compiler, and linker feeding the specified source code files. A compiler converts source code into binary instruction for a specific processor's architecture, thus making it less portable . This conversion is made just once, on the developer's environment, and after that the same binary can be distributed to the user's machines where it can be executed without further translation. A cross compiler can generate binary code for

5928-515: The two main means by which programming languages are implemented, they are not mutually exclusive, as most interpreting systems also perform some translation work, just like compilers. The terms " interpreted language " or " compiled language " signify that the canonical implementation of that language is an interpreter or a compiler, respectively. A high-level language is ideally an abstraction independent of particular implementations. Interpreters were used as early as 1952 to ease programming within

6006-410: The user machine even if it has a different processor than the machine where the code is compiled. An interpreted program can be distributed as source code. It needs to be translated in each final machine, which takes more time but makes the program distribution independent of the machine's architecture. However, the portability of interpreted source code is dependent on the target machine actually having

6084-532: Was initially a research and development-oriented project. Reaching a mature state of development and an official 1.0 release in mid-2007, its next focus was on releasing a production-ready version with more CPython compatibility. Many of PyPy's changes have been made during coding sprints . PyPy was funded by the European Union being a Specific Targeted Research Project between December 2004 and March 2007. In June 2008, PyPy announced funding being part of

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