Hypercube (communication pattern): Difference between revisions

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<math>d</math>-dimensional '''[[hypercube]]''' is a network topology for parallel computers with <math>2^d</math> processing elements. The topology allows for an efficient implementation of some basic communication primitives such as [[Broadcasting (networking)|Broadcast]], All-[[Reduce (parallel pattern)|Reduce]], and [[Prefix sum]].<ref>Grama, A.(2003). Introduction to Parallel Computing. Addison Wesley; Auflage: 2 ed. {{ISBN|978-0201648652}}.</ref> The processing elements are numbered <math>0</math> through <math>2^d - 1</math>. Each processing element is adjacent to processing elements whose numbers differ in one and only one bit. The algorithms described in this page utilize this structure efficiently.

== Algorithm Outlineoutline ==

Most of the communication primitives presented in this article share a common template.<ref>Foster, I.(1995). Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Addison Wesley; {{ISBN|0201575949}}.</ref> Initially, each processing element possesses one message that must reach every other processing element during the course of the algorithm. The following pseudo code sketches the communication steps necessary. Hereby, '''Initialization''', '''Operation''', and '''Output''' are placeholders that depend on the given communication primitive (see next section).

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[[File:Hypergraph Communication Steps for Prefix Sum.png|thumb|Example for a prefix sum calculation. Upper number: tentatetive prefix sum (variable <math>x</math>). Lower number: sum over all elements in the sub cube (variable <math>\sigma</math>).]]

=== All-Gathergather / Allall-Reducereduce ===

'''All-Gathergather''' operations start with each processing element having a message <math>m_i</math>. The goal of the operation is for each processing element to know the messages of all other processing elements, i.e. <math>x := m_0 \cdot m_1 \dots m_p</math> where <math>\cdot</math> is concatenation. The operation can be implemented following the algorithm template.

'''Input''': message <math>x := m_i</math> at processing unit <math>i</math>.

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The same principle can be applied to the '''All-Reduce''' operations, but instead of concatenating the messages, it performs a reduction operation on the two messages. So it is a '''Reduce''' operation, where all processing units know the result. Compared to a normal reduce operation followed by a broadcast, All-Reduce in hypercubes reduces the number of communication steps.

=== All-to-Allall ===

Here every processing element has a unique message for all other processing elements.

'''Input:''' message <math>m_{ij}</math> at processing element <math>i</math> to processing element <math>j</math>.

'''for''' <math>d > k \geq 0</math> '''do'''

'''Receive''' from processing element <math>i \text{ XOR } 2^k</math>:

all messages for my <math>k</math>-dimensional sub cube

'''Send''' to processing element <math>i \text{ XOR } 2^k</math>:

all messages for its <math>k</math>-dimensional sub cube

'''endfor'''

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This results in a run-time of <math>T(n,p) \approx \log{p} (T_\text{start} + \frac{p}{2}nT_\text{byte})</math>.

== ESBT-Broadcastbroadcast ==

The ESBT-broadcast (Edge-disjoint Spanning Binomial Tree) algorithm<ref>{{cite journal|last1=Johnsson|first1=S.L.|last2=Ho|first2=C.-T.|title=Optimum broadcasting and personalized communication in hypercubes|journal=IEEE Transactions on Computers|volume=38|issue=9|year=1989|pages=1249–1268|issn=0018-9340|doi=10.1109/12.29465}}</ref> is a pipelined broadcast algorithm with optimal runtime for clusters with hypercube network topology. The algorithm embeds <math>d</math> edge-disjoint binomial trees in the hypercube, such that each neighbor of processing element <math>0</math> is the root of a spanning binomial tree on <math>2^d - 1</math> nodes. To broadcast a message, the source node splits its message into <math>k</math> chunks of equal size and cyclically sends them to the roots of the binomial trees. Upon receiving a chunk, the binomial trees broadcast it.

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In each step, the source node sends one of its <math>k</math> chunks to a binomial tree. Broadcasting the chunk within the binomial tree takes <math>d</math> steps. Thus, it takes <math>k</math> steps to distribute all chunks and additionally <math>d</math> steps until the last binomial tree broadcast has finished, resulting in <math>k + d</math> steps overall. Therefore, the runtime for a message of length <math>n</math> is <math>T(n, p, k) = \left(\frac{n}{k} T_\text{byte} + T_\text{start} \right) (k + d)</math>. With the optimal chunk size <math>k^* = \sqrt{\frac{nd \cdot T_\text{byte}}{T_\text{start}}}</math>, the optimal runtime of the algorithm is <math>T^*(n, p) = n \cdot T_\text{byte} + \log(p) \cdot T_\text{start} + \sqrt{n \log(p) \cdot T_\text{start} \cdot T_\text{byte}}</math>.

=== Construction of the Binomialbinomial Treestrees ===

[[File:HypergraphESBT.png|thumb|A <math>3</math>-dimensional hypercubes with three ESBT embedded.]]