498 research outputs found
On the Complexity of List Ranking in the Parallel External Memory Model
We study the problem of list ranking in the parallel external memory (PEM)
model. We observe an interesting dual nature for the hardness of the problem
due to limited information exchange among the processors about the structure of
the list, on the one hand, and its close relationship to the problem of
permuting data, which is known to be hard for the external memory models, on
the other hand.
By carefully defining the power of the computational model, we prove a
permuting lower bound in the PEM model. Furthermore, we present a stronger
\Omega(log^2 N) lower bound for a special variant of the problem and for a
specific range of the model parameters, which takes us a step closer toward
proving a non-trivial lower bound for the list ranking problem in the
bulk-synchronous parallel (BSP) and MapReduce models. Finally, we also present
an algorithm that is tight for a larger range of parameters of the model than
in prior work
Ramanujan Coverings of Graphs
Let be a finite connected graph, and let be the spectral radius of
its universal cover. For example, if is -regular then
. We show that for every , there is an -covering
(a.k.a. an -lift) of where all the new eigenvalues are bounded from
above by . It follows that a bipartite Ramanujan graph has a Ramanujan
-covering for every . This generalizes the case due to Marcus,
Spielman and Srivastava (2013).
Every -covering of corresponds to a labeling of the edges of by
elements of the symmetric group . We generalize this notion to labeling
the edges by elements of various groups and present a broader scenario where
Ramanujan coverings are guaranteed to exist.
In particular, this shows the existence of richer families of bipartite
Ramanujan graphs than was known before. Inspired by Marcus-Spielman-Srivastava,
a crucial component of our proof is the existence of interlacing families of
polynomials for complex reflection groups. The core argument of this component
is taken from a recent paper of them (2015).
Another important ingredient of our proof is a new generalization of the
matching polynomial of a graph. We define the -th matching polynomial of
to be the average matching polynomial of all -coverings of . We show this
polynomial shares many properties with the original matching polynomial. For
example, it is real rooted with all its roots inside .Comment: 38 pages, 4 figures, journal version (minor changes from previous
arXiv version). Shortened version appeared in STOC 201
Fast parallel permutation algorithms
We investigate the problem of permuting n data items on an EREW PRAM with p processors using little additional storage. We present a simple algorithm with run time O((n/p)log n) and an improved algorithm with run time O(n/p+log nloglog(n/p)). Both algorithms require n additional global bits and O(1) local storage per processor. If prefix summation is supported at the instruction level, the run time of the improved algorithm is O(n/p). The algorithms can be used to rehash the address space of a PRAM emulation
External-Memory Graph Algorithms
We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include:
Proximate-neighboring. We present a simple
method for deriving external-memory lower bounds
via reductions from a problem we call the “proximate neighbors” problem. We use this technique to derive non-trivial lower bounds for such problems as list ranking, expression tree evaluation, and connected components. PRAM simulation. We give methods for efficiently
simulating PRAM computations in external memory, even for some cases in which the PRAM algorithm is not work-optimal. We apply this to derive a number of optimal (and simple) external-memory graph algorithms.
Time-forward processing. We present a general
technique for evaluating circuits (or “circuit-like”
computations) in external memory. We also usethis in a deterministic list ranking algorithm.
Deterministic 3-coloring of a cycle. We give
several optimal methods for 3-coloring a cycle,
which can be used as a subroutine for finding large
independent sets for list ranking. Our ideas go
beyond a straightforward PRAM simulation, and
may be of independent interest.
External depth-first search. We discuss a method
for performing depth first search and solving related
problems efficiently in external memory. Our
technique can be used in conjunction with ideas
due to Ullman and Yannakakis in order to solve
graph problems involving closed semi-ring computations even when their assumption that vertices fit in main memory does not hold.
Our techniques apply to a number of problems, including list ranking, which we discuss in detail, finding Euler tours, expression-tree evaluation, centroid decomposition of a tree, least-common ancestors, minimum spanning tree verification, connected and biconnected components, minimum spanning forest, ear decomposition, topological sorting, reachability, graph drawing, and visibility representation
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques
Sorting signed permutations by reversals, revisited
AbstractThe problem of sorting signed permutations by reversals (SBR) is a fundamental problem in computational molecular biology. The goal is, given a signed permutation, to find a shortest sequence of reversals that transforms it into the positive identity permutation, where a reversal is the operation of taking a segment of the permutation, reversing it, and flipping the signs of its elements.In this paper we describe a randomized algorithm for SBR. The algorithm tries to sort the permutation by repeatedly performing a random oriented reversal. This process is in fact a random walk on the graph where permutations are the nodes and an arc from π to π′ corresponds to an oriented reversal that transforms π to π′. We show that if this random walk stops at the identity permutation, then we have found a shortest sequence. We give empirical evidence that this process indeed succeeds with high probability on a random permutation.To implement our algorithm we describe a data structure to maintain a permutation, that allows to draw an oriented reversal uniformly at random, and perform it in sub-linear time. With this data structure we can implement the random walk in O(n3/2logn) time, thus obtaining an algorithm for SBR that almost always runs in sub-quadratic time. The data structures we present may also be of independent interest for developing other algorithms for SBR, and for other problems.Finally, we present the first efficient parallel algorithm for SBR. We obtain this result by developing a fast implementation of the recent algorithm of Bergeron (Proceedings of CPM, 2001, pp. 106–117) for sorting signed permutations by reversals that is parallelizable. Our implementation runs in O(n2logn) time on a regular RAM, and in O(nlogn) time on a PRAM using n processors
Fast integer merging on the EREW PRAM
We investigate the complexity of merging sequences of small integers on the EREW PRAM. Our most surprising result is that two sorted sequences of bits each can be merged in time. More generally, we describe an algorithm to merge two sorted sequences of integers drawn from the set in time using an optimal number of processors. No sublogarithmic merging algorithm for this model of computation was previously known. The algorithm not only produces the merged sequence, but also computes the rank of each input element in the merged sequence. On the other hand, we show a lower bound of on the time needed to merge two sorted sequences of length each with elements in the set , implying that our merging algorithm is as fast as possible for . If we impose an additional stability condition requiring the ranks of each input sequence to form an increasing sequence, then the time complexity of the problem becomes , even for . Stable merging is thus harder than nonstable merging
The Melbourne Shuffle: Improving Oblivious Storage in the Cloud
We present a simple, efficient, and secure data-oblivious randomized shuffle
algorithm. This is the first secure data-oblivious shuffle that is not based on
sorting. Our method can be used to improve previous oblivious storage solutions
for network-based outsourcing of data
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