Merge algorithm: Difference between revisions

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{{Short description|Algorithm that combines multiple sorted lists into one}}

'''Merge algorithms''' are a family of [[algorithm]]s that take multiple [[sorting algorithm|sorted]] lists as input and produce a single list as output, containing all the elements of the inputs lists in sorted order. These algorithms are used as [[subroutine]]s in various [[sorting algorithm]]s, most famously [[merge sort]].

== Application ==

[[File:Merge sort algorithm diagram.svg|thumb|upright=1.5|AnA examplegraph forexemplifying merge sort. Two red arrows starting from the same node indicate a split, while two green arrows ending at the same node correspond to an execution of the merge algorithm.]]

The merge algorithm plays a critical role in the [[merge sort]] algorithm, a [[comparison sort|comparison-based sorting algorithm]]. Conceptually, the merge sort algorithm consists of two steps:

# [[Recursion (computer science)|Recursively]] divide the list into sublists of (roughly) equal length, until each sublist contains only one element, or in the case of iterative (bottom up) merge sort, consider a list of ''n'' elements as ''n'' sub-lists of size 1. A list containing a single element is, by definition, sorted.

# Repeatedly merge sublists to create a new sorted sublist until the single list contains all elements. The single list is the sorted list.

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== Merging two lists ==

Merging two sorted lists into one can be done in [[linear time]] and linear or constant space (depending on the data access model). The following [[pseudocode]] demonstrates an algorithm that merges input lists (either [[linked list]]s or [[Array data structure|arrays]]) {{mvar|A}} and {{mvar|B}} into a new list {{mvar|C}}.<ref name="skiena">{{cite book |last=Skiena |first=Steven |authorlinkauthor-link=Steven Skiena |title=The Algorithm Design Manual |publisher=[[Springer Science+Business Media]] |edition=2nd |year=2010 |isbn=978-1-849-96720-24 |page=123}}</ref>{{r|toolbox}}{{rp|104}} The function {{mono|head}} yields the first element of a list; "dropping" an element means removing it from its list, typically by incrementing a pointer or index.

'''algorithm''' merge(A, B) '''is'''

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When the inputs are linked lists, this algorithm can be implemented to use only a constant amount of working space; the pointers in the lists' nodes can be reused for bookkeeping and for constructing the final merged list.

In the merge sort algorithm, this [[subroutine]] is typically used to merge two sub-arrays {{mono|A[lo..mid]}}, {{mono|A[mid+1..hi]}} of a single array {{mono|A}}. This can be done by copying the sub-arrays into a temporary array, then applying the merge algorithm above.{{r|skiena}} The allocation of a temporary array can be avoided, but at the expense of speed and programming ease. Various in-place merge algorithms have been devised,<ref>{{cite journal |last1=Katajainen |first1=Jyrki |first2=Tomi |last2=Pasanen |first3=Jukka |last3=Teuhola |title=Practical in-place mergesort |journal=Nordic J. Computing |volume=3 |issue=1 |year=1996 |pages=27–40 |citeseerx=10.1.1.22.8523}}</ref> sometimes sacrificing the linear-time bound to produce an {{math|''O''(''n'' log ''n'')}} algorithm;<ref>{{Cite conference| doi = 10.1007/978-3-540-30140-0_63| title = Stable Minimum Storage Merging by Symmetric Comparisons| conference = European Symp. Algorithms| volume = 3221| pages = 714–723| series = Lecture Notes in Computer Science| year = 2004| last1 = Kim | first1 = Pok-Son| last2 = Kutzner | first2 = Arne| isbn = 978-3-540-23025-0| citeseerx=10.1.1.102.4612}}</ref> see {{slink|Merge sort|Variants}} for discussion.

==K-way merging==

{{Merge from|K-way merge algorithm |discuss=Talk:Merge algorithm#Merge K-way merge algorithm article into this one |date=December 2017 |section=yes}}

{{Main|K-way merge algorithm}}

{{mvar|k}}-way merging generalizes binary merging to an arbitrary number {{mvar|k}} of sorted input lists. Applications of {{mvar|k}}-way merging arise in various sorting algorithms, including [[patience sorting]]<ref name="Chandramouli">{{Cite conference |last1=Chandramouli |first1=Badrish |last2=Goldstein |first2=Jonathan |title=Patience is a Virtue: Revisiting Merge and Sort on Modern Processors |conference=SIGMOD/PODS |year=2014}}</ref> and an [[external sorting]] algorithm that divides its input into {{math|''k'' {{=}} {{sfrac|1|''M''}} − 1}} blocks that fit in memory, sorts these one by one, then merges these blocks.{{r|toolbox}}{{rp|119–120}}

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</div>

Searching for the next smallest element to be output (find-min) and restoring heap order can now be done in {{math|''O''(log ''k'')}} time (more specifically, {{math|2⌊log ''k''⌋}} comparisons{{r|greene}}), and the full problem can be solved in {{math|''O''(''n'' log ''k'')}} time (approximately {{math|2''n''⌊log ''k''⌋}} comparisons).{{r|greene}}<ref name="toolbox">{{cite book|author1=[[Kurt Mehlhorn]]|author-link=Kurt Mehlhorn|author2=[[Peter Sanders|author2-link=Peter Sanders (computer scientist)|Peter Sanders]]|title=Algorithms and Data Structures: The Basic Toolbox |date=2008 |publisher=Springer |isbn=978-3-540-77978-0 |url=http://people.mpi-inf.mpg.de/~mehlhorn/ftp/Toolbox/}}</ref>{{rp|119–120}}

A third algorithm for the problem is a [[divide and conquer algorithm|divide and conquer]] solution that builds on the binary merge algorithm:

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== Parallel merge ==

A [[task parallelism|parallel]] version of the binary merge algorithm can serve as a building block of a [[Merge sort#Parallel merge sort|parallel merge sort]]. The following pseudocode demonstrates this algorithm in a [[fork-joinfork–join model|parallel divide-and-conquer]] style (adapted from Cormen ''et al.''<ref name="clrs">{{Introduction to Algorithms|3}}</ref>{{rp|800}}). It operates on two sorted arrays {{mvar|A}} and {{mvar|B}} and writes the sorted output to array {{mvar|C}}. The notation {{mono|A[i...j]}} denotes the part of {{mvar|A}} from index {{mvar|i}} through {{mvar|j}}, exclusive.

'''algorithm''' merge(A[i...j], B[k...ℓ], C[p...q]) '''is'''

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merge(A[r+1...j], B[s...ℓ], C[t+1...q])

The algorithm operates by splitting either {{mvar|A}} or {{mvar|B}}, whichever is larger, into (nearly) equal halves. It then splits the other array into a part thatwith isvalues smaller than the midpoint of the first, and a part that iswith larger or equal values. (The [[binary search]] subroutine returns the index in {{mvar|B}} where {{math|''A''[''r'']}} would be, if it were in {{mvar|B}}; that this always a number between {{mvar|k}} and {{mvar|ℓ}}.) Finally, each pair of halves is merged [[Divide and conquer algorithm|recursively]], and since the recursive calls are independent of each other, they can be done in parallel. Hybrid approach, where serial algorithm is used for recursion base case has been shown to perform well in practice <ref name="vjd">{{citation| author=Victor J. Duvanenko| title=Parallel Merge| journal=Dr. Dobb's Journal| date=2011| url=http://www.drdobbs.com/parallel/parallel-merge/229204454}}</ref>

The [[Analysis of parallel algorithms#Overview|work]] performed by the algorithm for two arrays holding a total of {{mvar|n}} elements, i.e., the running time of a serial version of it, is {{math|''O''(''n'')}}. This is optimal since {{mvar|n}} elements need to be copied into {{mvar|C}}. ItsTo criticalcalculate paththe length,[[Analysis howeverof parallel algorithms#Overview|span]] of the algorithm, it is {{math|Θ(log²necessary to derive a [[Recurrence relation]]. Since the two recursive calls of ''nmerge'')}} are in parallel, meaningonly thatthe itcostlier takesof thatthe muchtwo timecalls onneeds anto idealbe machineconsidered. withIn anthe worst case, the unboundedmaximum number of processorselements in one of the recursive calls is at most <math display="inline">\frac 3 4 n</math> since the array with more elements is perfectly split in half.{{r|clrs}}{{rp|801–802}} Adding the <math>\Theta\left( \log(n)\right)</math> cost of the Binary Search, we obtain this recurrence as an upper bound:

<math>T_{\infty}^\text{merge}(n) = T_{\infty}^\text{merge}\left(\frac {3} {4} n\right) + \Theta\left( \log(n)\right)</math>

The solution is <math>T_{\infty}^\text{merge}(n) = \Theta\left(\log(n)^2\right)</math>, meaning that it takes that much time on an ideal machine with an unbounded number of processors.{{r|clrs}}{{rp|801–802}}

'''Note:''' The routine is not [[Sorting algorithm#Stability|stable]]: if equal items are separated by splitting {{mvar|A}} and {{mvar|B}}, they will become interleaved in {{mvar|C}}; also swapping {{mvar|A}} and {{mvar|B}} will destroy the order, if equal items are spread among both input arrays. As a result, when used for sorting, this algorithm produces a sort that is not stable.

== Parallel merge of two lists ==

There are also algorithms that introduce parallelism within a single instance of merging of two sorted lists. These can be used in field-programmable gate arrays ([[FPGA]]s), specialized sorting circuits, as well as in modern processors with single-instruction multiple-data ([[SIMD]]) instructions.

Existing parallel algorithms are based on modifications of the merge part of either the [[bitonic sorter]] or [[odd-even mergesort]].<ref name="flimsj">{{cite journal |last1=Papaphilippou |first1=Philippos |last2=Luk |first2=Wayne |last3=Brooks |first3=Chris |title=FLiMS: a Fast Lightweight 2-way Merger for Sorting |journal=IEEE Transactions on Computers |date=2022 |pages=1–12 |doi=10.1109/TC.2022.3146509|hdl=10044/1/95271 |s2cid=245669103 |hdl-access=free |arxiv=2112.05607 }}</ref> In 2018, Saitoh M. et al. introduced MMS <ref>{{cite book |last1=Saitoh |first1=Makoto |last2=Elsayed |first2=Elsayed A. |last3=Chu |first3=Thiem Van |last4=Mashimo |first4=Susumu |last5=Kise |first5=Kenji |title=2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |chapter=A High-Performance and Cost-Effective Hardware Merge Sorter without Feedback Datapath |date=April 2018 |pages=197–204 |doi=10.1109/FCCM.2018.00038|isbn=978-1-5386-5522-1 |s2cid=52195866 }}</ref> for FPGAs, which focused on removing a multi-cycle feedback datapath that prevented efficient pipelining in hardware. Also in 2018, Papaphilippou P. et al. introduced FLiMS <ref name="flimsj" /> that improved the hardware utilization and performance by only requiring <math>\log_2(P)+1</math> pipeline stages of {{math|''P/2''}} compare-and-swap units to merge with a parallelism of {{math|''P''}} elements per FPGA cycle.

== Language support ==

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=== Python ===

[[Python (programming language)|Python]]'s standard library (since 2.6) also has a {{mono|merge}} function in the {{mono|heapq}} module, that takes multiple sorted iterables, and merges them into a single iterator.<ref>{{cite web| url = https://docs.python.org/library/heapq.html#heapq.merge| title = heapq — Heap queue algorithm — Python 3.10.1 documentation}}</ref>

== See also ==

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== References ==

{{reflistReflist}}

== Further reading ==

* [[Donald Knuth]]. ''[[The Art of Computer Programming]]'', Volume 3: ''Sorting and Searching'', Third Edition. Addison-Wesley, 1997. {{ISBN|0-201-89685-0}}. Pages 158–160 of section 5.2.4: Sorting by Merging. Section 5.3.2: Minimum-Comparison Merging, pp.&nbsp;197–207.

==External links==

*[https://duvanenko.tech.blog/2018/05/23/faster-sorting-in-c/ High Performance Implementation] of Parallel and Serial Merge in [[C Sharp (programming language)|C#]] with source in [https://github.com/DragonSpit/HPCsharp/ GitHub] and in [[C++]] [https://github.com/DragonSpit/ParallelAlgorithms GitHub]

{{sorting}}