Misplaced Pages

#939060

64-524: RAPID is a high-level programming language used to control ABB industrial robots. RAPID was introduced along with the S4 Control System in 1994 by ABB , superseding the ARLA programming language. Features in the language include: This programming-language -related article is a stub . You can help Misplaced Pages by expanding it . High-level programming language In computer science ,

128-399: A bottleneck in a system – a component that is the limiting factor on performance. In terms of code, this will often be a hot spot  – a critical part of the code that is the primary consumer of the needed resource – though it can be another factor, such as I/O latency or network bandwidth. In computer science, resource consumption often follows

192-433: A high-level programming language is a programming language with strong abstraction from the details of the computer . In contrast to low-level programming languages , it may use natural language elements , be easier to use, or may automate (or even hide entirely) significant areas of computing systems (e.g. memory management ), making the process of developing a program simpler and more understandable than when using

256-447: A type safe alternative in many cases. In both cases, the inlined function body can then undergo further compile-time optimizations by the compiler, including constant folding , which may move some computations to compile time. In many functional programming languages, macros are implemented using parse-time substitution of parse trees/abstract syntax trees, which it is claimed makes them safer to use. Since in many cases interpretation

320-479: A complete rewrite if they need to be changed. Thus optimization can typically proceed via refinement from higher to lower, with initial gains being larger and achieved with less work, and later gains being smaller and requiring more work. However, in some cases overall performance depends on performance of very low-level portions of a program, and small changes at a late stage or early consideration of low-level details can have outsized impact. Typically some consideration

384-481: A filtering program will commonly read each line and filter and output that line immediately. This only uses enough memory for one line, but performance is typically poor, due to the latency of each disk read. Caching the result is similarly effective, though also requiring larger memory use. Optimization can reduce readability and add code that is used only to improve the performance . This may complicate programs or systems, making them harder to maintain and debug. As

448-428: A focus on usability over optimal program efficiency. Unlike low-level assembly languages , high-level languages have few, if any, language elements that translate directly into a machine's native opcodes . Other features, such as string handling routines, object-oriented language features, and file input/output, may also be present. One thing to note about high-level programming languages is that these languages allow

512-575: A form of power law distribution, and the Pareto principle can be applied to resource optimization by observing that 80% of the resources are typically used by 20% of the operations. In software engineering, it is often a better approximation that 90% of the execution time of a computer program is spent executing 10% of the code (known as the 90/10 law in this context). More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data —

576-453: A fully general lambda abstraction in a programming language for the first time. "High-level language" refers to the higher level of abstraction from machine language . Rather than dealing with registers, memory addresses, and call stacks, high-level languages deal with variables, arrays, objects , complex arithmetic or Boolean expressions , subroutines and functions, loops, threads , locks, and other abstract computer science concepts, with

640-421: A good choice of efficient algorithms and data structures , and efficient implementation of these algorithms and data structures comes next. After design, the choice of algorithms and data structures affects efficiency more than any other aspect of the program. Generally data structures are more difficult to change than algorithms, as a data structure assumption and its performance assumptions are used throughout

704-536: A lower-level language. The amount of abstraction provided defines how "high-level" a programming language is. In the 1960s, a high-level programming language using a compiler was commonly called an autocode . Examples of autocodes are COBOL and Fortran . The first high-level programming language designed for computers was Plankalkül , created by Konrad Zuse . However, it was not implemented in his time, and his original contributions were largely isolated from other developments due to World War II , aside from

SECTION 10

#1732780015940

768-425: A mathematical formula like: The optimization, sometimes performed automatically by an optimizing compiler, is to select a method ( algorithm ) that is more computationally efficient, while retaining the same functionality. See algorithmic efficiency for a discussion of some of these techniques. However, a significant improvement in performance can often be achieved by removing extraneous functionality. Optimization

832-407: A modular system may allow rewrite of only some component – for example, a Python program may rewrite performance-critical sections in C. In a distributed system, choice of architecture ( client-server , peer-to-peer , etc.) occurs at the design level, and may be difficult to change, particularly if all components cannot be replaced in sync (e.g., old clients). Given an overall design,

896-498: A result, optimization or performance tuning is often performed at the end of the development stage . Donald Knuth made the following two statements on optimization: "We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%" (He also attributed the quote to Tony Hoare several years later, although this might have been an error as Hoare disclaims having coined

960-623: A specific system architecture . Abstraction penalty is the cost that high-level programming techniques pay for being unable to optimize performance or use certain hardware because they don't take advantage of certain low-level architectural resources. High-level programming exhibits features like more generic data structures and operations, run-time interpretation, and intermediate code files; which often result in execution of far more operations than necessary, higher memory consumption, and larger binary program size. For this reason, code which needs to run particularly quickly and efficiently may require

1024-427: A system, which can cause problems from memory use, and correctness issues from stale caches. Beyond general algorithms and their implementation on an abstract machine, concrete source code level choices can make a significant difference. For example, on early C compilers, while(1) was slower than for(;;) for an unconditional loop, because while(1) evaluated 1 and then had a conditional jump which tested if it

1088-403: A trade-off – where one factor is optimized at the expense of others. For example, increasing the size of cache improves run time performance, but also increases the memory consumption. Other common trade-offs include code clarity and conciseness. There are instances where the programmer performing the optimization must decide to make the software better for some operations but at

1152-443: Is also true that advances in hardware will more often than not obviate any potential improvements, yet the obscuring code will persist into the future long after its purpose has been negated. Optimization during code development using macros takes on different forms in different languages. In some procedural languages, such as C and C++ , macros are implemented using token substitution. Nowadays, inline functions can be used as

1216-441: Is given to efficiency throughout a project – though this varies significantly – but major optimization is often considered a refinement to be done late, if ever. On longer-running projects there are typically cycles of optimization, where improving one area reveals limitations in another, and these are typically curtailed when performance is acceptable or gains become too small or costly. As performance

1280-440: Is incorrect, because the code is complicated by the optimization and the programmer is distracted by optimizing. When deciding whether to optimize a specific part of the program, Amdahl's Law should always be considered: the impact on the overall program depends very much on how much time is actually spent in that specific part, which is not always clear from looking at the code without a performance analysis . A better approach

1344-498: Is inherently at a slightly higher level than the microcode or micro-operations used internally in many processors. There are three general modes of execution for modern high-level languages: Note that languages are not strictly interpreted languages or compiled languages. Rather, implementations of language behavior use interpreting or compiling. For example, ALGOL 60 and Fortran have both been interpreted (even though they were more typically compiled). Similarly, Java shows

SECTION 20

#1732780015940

1408-916: Is not always an obvious or intuitive process. In the example above, the "optimized" version might actually be slower than the original version if N were sufficiently small and the particular hardware happens to be much faster at performing addition and looping operations than multiplication and division. In some cases, however, optimization relies on using more elaborate algorithms, making use of "special cases" and special "tricks" and performing complex trade-offs. A "fully optimized" program might be more difficult to comprehend and hence may contain more faults than unoptimized versions. Beyond eliminating obvious antipatterns, some code level optimizations decrease maintainability. Optimization will generally focus on improving just one or two aspects of performance: execution time, memory usage, disk space, bandwidth, power consumption or some other resource. This will usually require

1472-457: Is part of the specification of a program – a program that is unusably slow is not fit for purpose: a video game with 60 Hz (frames-per-second) is acceptable, but 6 frames-per-second is unacceptably choppy – performance is a consideration from the start, to ensure that the system is able to deliver sufficient performance, and early prototypes need to have roughly acceptable performance for there to be confidence that

1536-460: Is possible for a high-level language to be directly implemented by a computer – the computer directly executes the HLL code. This is known as a high-level language computer architecture – the computer architecture itself is designed to be targeted by a specific high-level language. The Burroughs large systems were target machines for ALGOL 60 , for example. Program optimization Although

1600-457: Is similar to a static "average case" analog of the dynamic technique of adaptive optimization. Self-modifying code can alter itself in response to run time conditions in order to optimize code; this was more common in assembly language programs. Some CPU designs can perform some optimizations at run time. Some examples include out-of-order execution , speculative execution , instruction pipelines , and branch predictors . Compilers can help

1664-436: Is the use of a fast path for common cases, improving performance by avoiding unnecessary work. For example, using a simple text layout algorithm for Latin text, only switching to a complex layout algorithm for complex scripts, such as Devanagari . Another important technique is caching, particularly memoization , which avoids redundant computations. Because of the importance of caching, there are often many levels of caching in

1728-436: Is therefore to design first, code from the design and then profile / benchmark the resulting code to see which parts should be optimized. A simple and elegant design is often easier to optimize at this stage, and profiling may reveal unexpected performance problems that would not have been addressed by premature optimization. In practice, it is often necessary to keep performance goals in mind when first designing software, but

1792-495: Is used, that is one way to ensure that such computations are only performed at parse-time, and sometimes the only way. Lisp originated this style of macro, and such macros are often called "Lisp-like macros". A similar effect can be achieved by using template metaprogramming in C++ . In both cases, work is moved to compile-time. The difference between C macros on one side, and Lisp-like macros and C++ template metaprogramming on

1856-520: The C language , and similar languages, were most often considered "high-level", as it supported concepts such as expression evaluation, parameterised recursive functions, and data types and structures, while assembly language was considered "low-level". Today, many programmers might refer to C as low-level, as it lacks a large runtime-system (no garbage collection, etc.), basically supports only scalar operations, and provides direct memory addressing; it therefore, readily blends with assembly language and

1920-474: The Intel 432 (1981); or ones that take years of work to achieve acceptable performance, such as Java (1995), which only achieved acceptable performance with HotSpot (1999). The degree to which performance changes between prototype and production system, and how amenable it is to optimization, can be a significant source of uncertainty and risk. At the highest level, the design may be optimized to make best use of

1984-545: The executable program is optimized at least as much as the compiler can predict. At the lowest level, writing code using an assembly language , designed for a particular hardware platform can produce the most efficient and compact code if the programmer takes advantage of the full repertoire of machine instructions . Many operating systems used on embedded systems have been traditionally written in assembler code for this reason. Programs (other than very small programs) are seldom written from start to finish in assembly due to

RAPID - Misplaced Pages Continue

2048-420: The system architecture which they were written for without major revision. This is the engineering 'trade-off' for the 'Abstraction Penalty'. Examples of high-level programming languages in active use today include Python , JavaScript , Visual Basic , Delphi , Perl , PHP , ECMAScript , Ruby , C# , Java and many others. The terms high-level and low-level are inherently relative. Some decades ago,

2112-413: The amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premium, one might deliberately choose a slower algorithm in order to use less memory. Often there is no "one size fits all" design which works well in all cases, so engineers make trade-offs to optimize the attributes of greatest interest. Additionally,

2176-453: The available resources, given goals, constraints, and expected use/load. The architectural design of a system overwhelmingly affects its performance. For example, a system that is network latency-bound (where network latency is the main constraint on overall performance) would be optimized to minimize network trips, ideally making a single request (or no requests, as in a push protocol ) rather than multiple roundtrips. Choice of design depends on

2240-408: The code written today is intended to run on as many machines as possible. As a consequence, programmers and compilers don't always take advantage of the more efficient instructions provided by newer CPUs or quirks of older models. Additionally, assembly code tuned for a particular processor without using such instructions might still be suboptimal on a different processor, expecting a different tuning of

2304-411: The code. Typically today rather than writing in assembly language, programmers will use a disassembler to analyze the output of a compiler and change the high-level source code so that it can be compiled more efficiently, or understand why it is inefficient. Just-in-time compilers can produce customized machine code based on run-time data, at the cost of compilation overhead. This technique dates to

2368-416: The constant factors matter: an asymptotically slower algorithm may be faster or smaller (because simpler) than an asymptotically faster algorithm when they are both faced with small input, which may be the case that occurs in reality. Often a hybrid algorithm will provide the best performance, due to this tradeoff changing with size. A general technique to improve performance is to avoid work. A good example

2432-442: The cost of making other operations less efficient. These trade-offs may sometimes be of a non-technical nature – such as when a competitor has published a benchmark result that must be beaten in order to improve commercial success but comes perhaps with the burden of making normal usage of the software less efficient. Such changes are sometimes jokingly referred to as pessimizations . Optimization may include finding

2496-473: The difficulty of trying to apply these labels to languages, rather than to implementations; Java is compiled to bytecode which is then executed by either interpreting (in a Java virtual machine (JVM)) or compiling (typically with a just-in-time compiler such as HotSpot , again in a JVM). Moreover, compiling, transcompiling, and interpreting is not strictly limited to only a description of the compiler artifact (binary executable or IL assembly). Alternatively, it

2560-448: The earliest regular expression engines, and has become widespread with Java HotSpot and V8 for JavaScript. In some cases adaptive optimization may be able to perform run time optimization exceeding the capability of static compilers by dynamically adjusting parameters according to the actual input or other factors. Profile-guided optimization is an ahead-of-time (AOT) compilation optimization technique based on run time profiles, and

2624-399: The effort required to make a piece of software completely optimal – incapable of any further improvement – is almost always more than is reasonable for the benefits that would be accrued; so the process of optimization may be halted before a completely optimal solution has been reached. Fortunately, it is often the case that the greatest improvements come early in

RAPID - Misplaced Pages Continue

2688-405: The final system will (with optimization) achieve acceptable performance. This is sometimes omitted in the belief that optimization can always be done later, resulting in prototype systems that are far too slow – often by an order of magnitude or more – and systems that ultimately are failures because they architecturally cannot achieve their performance goals, such as

2752-482: The goal of aggregating the most popular constructs with new or improved features. An example of this is Scala which maintains backward compatibility with Java , meaning that programs and libraries written in Java will continue to be usable even if a programming shop switches to Scala; this makes the transition easier and the lifespan of such high-level coding indefinite. In contrast, low-level programs rarely survive beyond

2816-425: The goals: when designing a compiler , if fast compilation is the key priority, a one-pass compiler is faster than a multi-pass compiler (assuming same work), but if speed of output code is the goal, a slower multi-pass compiler fulfills the goal better, even though it takes longer itself. Choice of platform and programming language occur at this level, and changing them frequently requires a complete rewrite, though

2880-611: The higher abstraction may allow for more powerful techniques providing better overall results than their low-level counterparts in particular settings. High-level languages are designed independent of a specific computing system architecture . This facilitates executing a program written in such a language on any computing system with compatible support for the Interpreted or JIT program. High-level languages can be improved as their designers develop improvements. In other cases, new high-level languages evolve from one or more others with

2944-477: The language's influence on the "Superplan" language by Heinz Rutishauser and also to some degree ALGOL . The first significantly widespread high-level language was Fortran , a machine-independent development of IBM's earlier Autocode systems. The ALGOL family, with ALGOL 58 defined in 1958 and ALGOL 60 defined in 1960 by committees of European and American computer scientists, introduced recursion as well as nested functions under lexical scope . ALGOL 60

3008-534: The machine level of CPUs and microcontrollers . Also, in the introduction chapter of The C Programming Language (second edition) by Brian Kernighan and Dennis Ritchie , C is described as "not a very high level" language. Assembly language may itself be regarded as a higher level (but often still one-to-one if used without macros ) representation of machine code , as it supports concepts such as constants and (limited) expressions, sometimes even variables, procedures, and data structures . Machine code , in turn,

3072-650: The need for auxiliary variables and can even result in faster performance by avoiding round-about optimizations. Between the source and compile level, directives and build flags can be used to tune performance options in the source code and compiler respectively, such as using preprocessor defines to disable unneeded software features, optimizing for specific processor models or hardware capabilities, or predicting branching , for instance. Source-based software distribution systems such as BSD 's Ports and Gentoo 's Portage can take advantage of this form of optimization. Use of an optimizing compiler tends to ensure that

3136-483: The optimal instruction scheduling might be different even on different processors of the same architecture. Computational tasks can be performed in several different ways with varying efficiency. A more efficient version with equivalent functionality is known as a strength reduction . For example, consider the following C code snippet whose intention is to obtain the sum of all integers from 1 to N : This code can (assuming no arithmetic overflow ) be rewritten using

3200-427: The other side, is that the latter tools allow performing arbitrary computations at compile-time/parse-time, while expansion of C macros does not perform any computation, and relies on the optimizer ability to perform it. Additionally, C macros do not directly support recursion or iteration , so are not Turing complete . As with any optimization, however, it is often difficult to predict where such tools will have

3264-435: The phrase. ) "In established engineering disciplines a 12% improvement, easily obtained, is never considered marginal and I believe the same viewpoint should prevail in software engineering" "Premature optimization" is a phrase used to describe a situation where a programmer lets performance considerations affect the design of a piece of code. This can result in a design that is not as clean as it could have been or code that

SECTION 50

#1732780015940

3328-424: The process. Even for a given quality metric (such as execution speed), most methods of optimization only improve the result; they have no pretense of producing optimal output. Superoptimization is the process of finding truly optimal output. Optimization can occur at a number of levels. Typically the higher levels have greater impact, and are harder to change later on in a project, requiring significant changes or

3392-407: The program take advantage of these CPU features, for example through instruction scheduling . Code optimization can be also broadly categorized as platform -dependent and platform-independent techniques. While the latter ones are effective on most or all platforms, platform-dependent techniques use specific properties of one platform, or rely on parameters depending on the single platform or even on

3456-622: The program, though this can be minimized by the use of abstract data types in function definitions, and keeping the concrete data structure definitions restricted to a few places. For algorithms, this primarily consists of ensuring that algorithms are constant O(1), logarithmic O(log n ), linear O( n ), or in some cases log-linear O( n log n ) in the input (both in space and time). Algorithms with quadratic complexity O( n ) fail to scale, and even linear algorithms cause problems if repeatedly called, and are typically replaced with constant or logarithmic if possible. Beyond asymptotic order of growth,

3520-440: The programmer balances the goals of design and optimization. Modern compilers and operating systems are so efficient that the intended performance increases often fail to materialize. As an example, caching data at the application level that is again cached at the operating system level does not yield improvements in execution. Even so, it is a rare case when the programmer will remove failed optimizations from production code. It

3584-620: The programmer to be detached and separated from the machine. That is, unlike low-level languages like assembly or machine language, high-level programming can amplify the programmer's instructions and trigger a lot of data movements in the background without their knowledge. The responsibility and power of executing instructions have been handed over to the machine from the programmer. High-level languages intend to provide features that standardize common tasks, permit rich debugging, and maintain architectural agnosticism; while low-level languages often produce more efficient code through optimization for

3648-404: The setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit, and thus a hybrid algorithm or adaptive algorithm may be faster than any single algorithm. A performance profiler can be used to narrow down decisions about which functionality fits which conditions. In some cases, adding more memory can help to make a program run faster. For example,

3712-508: The single processor. Writing or producing different versions of the same code for different processors might therefore be needed. For instance, in the case of compile-level optimization, platform-independent techniques are generic techniques (such as loop unrolling , reduction in function calls, memory efficient routines, reduction in conditions, etc.), that impact most CPU architectures in a similar way. A great example of platform-independent optimization has been shown with inner for loop, where it

3776-447: The time and cost involved. Most are compiled down from a high level language to assembly and hand optimized from there. When efficiency and size are less important large parts may be written in a high-level language. With more modern optimizing compilers and the greater complexity of recent CPUs , it is harder to write more efficient code than what the compiler generates, and few projects need this "ultimate" optimization step. Much of

3840-549: The use of a lower-level language, even if a higher-level language would make the coding easier. In many cases, critical portions of a program mostly in a high-level language can be hand-coded in assembly language , leading to a much faster, more efficient, or simply reliably functioning optimised program . However, with the growing complexity of modern microprocessor architectures, well-designed compilers for high-level languages frequently produce code comparable in efficiency to what most low-level programmers can produce by hand, and

3904-423: The word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system. A system can generally be made optimal not in absolute terms, but only with respect to a given quality metric, which may be in contrast with other possible metrics. As a result, the optimized system will typically only be optimal in one application or for one audience. One might reduce

SECTION 60

#1732780015940

3968-451: Was also the first language with a clear distinction between value and name-parameters and their corresponding semantics . ALGOL also introduced several structured programming concepts, such as the while-do and if-then-else constructs and its syntax was the first to be described in formal notation – Backus–Naur form (BNF). During roughly the same period, COBOL introduced records (also called structs) and Lisp introduced

4032-527: Was observed that a loop with an inner for loop performs more computations per unit time than a loop without it or one with an inner while loop. Generally, these serve to reduce the total instruction path length required to complete the program and/or reduce total memory usage during the process. On the other hand, platform-dependent techniques involve instruction scheduling, instruction-level parallelism , data-level parallelism, cache optimization techniques (i.e., parameters that differ among various platforms) and

4096-503: Was true, while for (;;) had an unconditional jump . Some optimizations (such as this one) can nowadays be performed by optimizing compilers . This depends on the source language, the target machine language, and the compiler, and can be both difficult to understand or predict and changes over time; this is a key place where understanding of compilers and machine code can improve performance. Loop-invariant code motion and return value optimization are examples of optimizations that reduce

#939060