170 research outputs found
A Deterministic Algorithm for the MST Problem in Constant Rounds of Congested Clique
In this paper, we show that the Minimum Spanning Tree problem can be solved
\emph{deterministically}, in rounds of the
model.
In the model, there are players
that perform computation in synchronous rounds. Each round consist of a phase
of local computation and a phase of communication, in which each pair of
players is allowed to exchange bit messages.
The studies of this model began with the MST problem: in the paper by Lotker
et al.[SPAA'03, SICOMP'05] that defines the
model the authors give a deterministic round algorithm that improved over a trivial round
adaptation of Bor\r{u}vka's algorithm.
There was a sequence of gradual improvements to this result: an
round algorithm by Hegeman et al. [PODC'15], an
round algorithm by Ghaffari and Parter, [PODC'16] and
an round algorithm by Jurdzi\'nski and Nowicki, [SODA'18], but
all those algorithms were randomized, which left the question about the
existence of any deterministic round algorithms for the
Minimum Spanning Tree problem open.
Our result resolves this question and establishes that
rounds is enough to solve the MST problem in the
model, even if we are not allowed to use any randomness.
Furthermore, the amount of communication needed by the algorithm makes it
applicable to some variants of the model
Super-Fast MST Algorithms in the Congested Clique Using o(m) Messages
In a sequence of recent results (PODC 2015 and PODC 2016), the running time of the fastest algorithm for the minimum spanning tree (MST) problem in the Congested Clique model was first improved to O(log(log(log(n)))) from O(log(log(n))) (Hegeman et al., PODC 2015) and then to O(log^*(n)) (Ghaffari and Parter, PODC 2016). All of these algorithms use Theta(n^2) messages independent of the number of edges in the input graph.
This paper positively answers a question raised in Hegeman et al., and presents the first "super-fast" MST algorithm with o(m) message complexity for input graphs with m edges. Specifically, we present an algorithm running in O(log^*(n)) rounds, with message complexity ~O(sqrt{m * n}) and then build on this algorithm to derive a family of algorithms, containing for any epsilon, 0 < epsilon <= 1, an algorithm running in O(log^*(n)/epsilon) rounds, using ~O(n^{1 + epsilon}/epsilon) messages. Setting epsilon = log(log(n))/log(n) leads to the first sub-logarithmic round Congested Clique MST algorithm that uses only ~O(n) messages.
Our primary tools in achieving these results are
(i) a component-wise bound on the number of candidates for MST edges, extending the sampling lemma of Karger, Klein, and Tarjan (Karger, Klein, and Tarjan, JACM 1995) and
(ii) Theta(log(n))-wise-independent linear graph sketches (Cormode and Firmani, Dist. Par. Databases, 2014) for generating MST candidate edges
Lessons from the Congested Clique Applied to MapReduce
The main results of this paper are (I) a simulation algorithm which, under
quite general constraints, transforms algorithms running on the Congested
Clique into algorithms running in the MapReduce model, and (II) a distributed
-coloring algorithm running on the Congested Clique which has an
expected running time of (i) rounds, if ;
and (ii) rounds otherwise. Applying the simulation theorem to
the Congested-Clique -coloring algorithm yields an -round
-coloring algorithm in the MapReduce model.
Our simulation algorithm illustrates a natural correspondence between
per-node bandwidth in the Congested Clique model and memory per machine in the
MapReduce model. In the Congested Clique (and more generally, any network in
the model), the major impediment to constructing fast
algorithms is the restriction on message sizes. Similarly, in the
MapReduce model, the combined restrictions on memory per machine and total
system memory have a dominant effect on algorithm design. In showing a fairly
general simulation algorithm, we highlight the similarities and differences
between these models.Comment: 15 page
Towards a complexity theory for the congested clique
The congested clique model of distributed computing has been receiving
attention as a model for densely connected distributed systems. While there has
been significant progress on the side of upper bounds, we have very little in
terms of lower bounds for the congested clique; indeed, it is now know that
proving explicit congested clique lower bounds is as difficult as proving
circuit lower bounds.
In this work, we use various more traditional complexity-theoretic tools to
build a clearer picture of the complexity landscape of the congested clique:
-- Nondeterminism and beyond: We introduce the nondeterministic congested
clique model (analogous to NP) and show that there is a natural canonical
problem family that captures all problems solvable in constant time with
nondeterministic algorithms. We further generalise these notions by introducing
the constant-round decision hierarchy (analogous to the polynomial hierarchy).
-- Non-constructive lower bounds: We lift the prior non-uniform counting
arguments to a general technique for proving non-constructive uniform lower
bounds for the congested clique. In particular, we prove a time hierarchy
theorem for the congested clique, showing that there are decision problems of
essentially all complexities, both in the deterministic and nondeterministic
settings.
-- Fine-grained complexity: We map out relationships between various natural
problems in the congested clique model, arguing that a reduction-based
complexity theory currently gives us a fairly good picture of the complexity
landscape of the congested clique
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