Editing Algorithmic efficiency (section)

===Implementation concerns===
Implementation issues can also have an effect on efficiency, such as the choice of programming language, or the way in which the algorithm is actually coded,<ref name="KriegelSchubert2016">{{cite journal|last1=Kriegel|first1=Hans-Peter|author-link=Hans-Peter Kriegel|last2=Schubert|first2=Erich|last3=Zimek|first3=Arthur|author-link3=Arthur Zimek|title=The (black) art of runtime evaluation: Are we comparing algorithms or implementations?|journal=Knowledge and Information Systems|volume=52|issue=2|year=2016|pages=341–378|issn=0219-1377|doi=10.1007/s10115-016-1004-2|s2cid=40772241}}</ref> or the choice of a [[compiler]] for a particular language, or the [[compiler optimization|compilation options]] used, or even the [[operating system]] being used. In many cases a language implemented by an [[interpreter (computing)|interpreter]] may be much slower than a language implemented by a compiler.<ref name="fourmilab.ch">{{cite web|url=http://www.fourmilab.ch/fourmilog/archives/2005-08/000567.html |title=Floating Point Benchmark: Comparing Languages (Fourmilog: None Dare Call It Reason) |publisher=Fourmilab.ch |date=4 August 2005 |access-date=14 December 2011}}</ref> See the articles on [[just-in-time compilation]] and [[interpreted language]]s.

There are other factors which may affect time or space issues, but which may be outside of a programmer's control; these include [[data alignment]], [[granularity#Data granularity|data granularity]], [[locality of reference|cache locality]], [[cache coherence|cache coherency]], [[garbage collection (computer science)|garbage collection]], [[instruction-level parallelism]], [[Multithreading (disambiguation)|multi-threading]]<!--Intentional link to DAB page--> (at either a hardware or software level), [[Simultaneous multithreading|simultaneous multitasking]], and [[subroutine]] calls.<ref name="steele1997">Guy Lewis Steele, Jr. "Debunking the 'Expensive Procedure Call' Myth, or, Procedure Call Implementations Considered Harmful, or, Lambda: The Ultimate GOTO". MIT AI Lab. AI Lab Memo AIM-443. October 1977.[http://dspace.mit.edu/handle/1721.1/5753]</ref>

Some processors have capabilities for [[vector processor|vector processing]], which allow a [[SIMD|single instruction to operate on multiple operands]]; it may or may not be easy for a programmer or compiler to use these capabilities. Algorithms designed for sequential processing may need to be completely redesigned to make use of [[parallel computing|parallel processing]], or they could be easily reconfigured. As [[parallel computing|parallel]] and [[distributed computing]] grow in importance in the late 2010s, more investments are being made into efficient [[high-level programming language|high-level]] [[Application programming interface|API]]s for parallel and distributed computing systems such as [[CUDA]], [[TensorFlow]], [[Apache Hadoop|Hadoop]], [[OpenMP]] and [[Message Passing Interface|MPI]].

Another problem which can arise in programming is that processors compatible with the same [[instruction set architecture|instruction set]] (such as [[x86-64]] or [[ARM architecture|ARM]]) may implement an instruction in different ways, so that instructions which are relatively fast on some models may be relatively slow on other models. This often presents challenges to [[optimizing compiler]]s, which must have extensive knowledge of the specific [[Central processing unit|CPU]] and other hardware available on the compilation target to best optimize a program for performance. In the extreme case, a compiler may be forced to [[software emulation|emulate]] instructions not supported on a compilation target platform, forcing it to [[code generation (compiler)|generate code]] or [[linking (computing)|link]] an external [[library (computing)|library call]] to produce a result that is otherwise incomputable on that platform, even if it is natively supported and more efficient in hardware on other platforms. This is often the case in [[embedded system]]s with respect to [[floating-point arithmetic]], where small and [[low-power computing|low-power]] [[microcontroller]]s often lack hardware support for floating-point arithmetic and thus require computationally expensive software routines to produce floating point calculations.