1,863 research outputs found
External Sampling
36th International Colloquium, ICALP 2009, Rhodes, Greece, July 5-12, 2009, Proceedings, Part IWe initiate the study of sublinear-time algorithms in the external memory model [1]. In this model, the data is stored in blocks of a certain size B, and the algorithm is charged a unit cost for each block access. This model is well-studied, since it reflects the computational issues occurring when the (massive) input is stored on a disk. Since each block access operates on B data elements in parallel, many problems have external memory algorithms whose number of block accesses is only a small fraction (e.g. 1/B) of their main memory complexity.
However, to the best of our knowledge, no such reduction in complexity is known for any sublinear-time algorithm. One plausible explanation is that the vast majority of sublinear-time algorithms use random sampling and thus exhibit no locality of reference. This state of affairs is quite unfortunate, since both sublinear-time algorithms and the external memory model are important approaches to dealing with massive data sets, and ideally they should be combined to achieve best performance.
In this paper we show that such combination is indeed possible. In particular, we consider three well-studied problems: testing of distinctness, uniformity and identity of an empirical distribution induced by data. For these problems we show random-sampling-based algorithms whose number of block accesses is up to a factor of 1/âB smaller than the main memory complexity of those problems. We also show that this improvement is optimal for those problems.
Since these problems are natural primitives for a number of sampling-based algorithms for other problems, our tools improve the external memory complexity of other problems as well.David & Lucile Packard Foundation (Fellowship)Center for Massive Data Algorithmics (MADALGO)Marie Curie (International Reintegration Grant 231077)National Science Foundation (U.S.) (Grant 0514771)National Science Foundation (U.S.) (Grant 0728645)National Science Foundation (U.S.) (Grant 0732334)Symantec Research Labs (Research Fellowship
Implicit Decomposition for Write-Efficient Connectivity Algorithms
The future of main memory appears to lie in the direction of new technologies
that provide strong capacity-to-performance ratios, but have write operations
that are much more expensive than reads in terms of latency, bandwidth, and
energy. Motivated by this trend, we propose sequential and parallel algorithms
to solve graph connectivity problems using significantly fewer writes than
conventional algorithms. Our primary algorithmic tool is the construction of an
-sized "implicit decomposition" of a bounded-degree graph on
nodes, which combined with read-only access to enables fast answers to
connectivity and biconnectivity queries on . The construction breaks the
linear-write "barrier", resulting in costs that are asymptotically lower than
conventional algorithms while adding only a modest cost to querying time. For
general non-sparse graphs on edges, we also provide the first writes
and operations parallel algorithms for connectivity and biconnectivity.
These algorithms provide insight into how applications can efficiently process
computations on large graphs in systems with read-write asymmetry
New Algorithms and Lower Bounds for Sequential-Access Data Compression
This thesis concerns sequential-access data compression, i.e., by algorithms
that read the input one or more times from beginning to end. In one chapter we
consider adaptive prefix coding, for which we must read the input character by
character, outputting each character's self-delimiting codeword before reading
the next one. We show how to encode and decode each character in constant
worst-case time while producing an encoding whose length is worst-case optimal.
In another chapter we consider one-pass compression with memory bounded in
terms of the alphabet size and context length, and prove a nearly tight
tradeoff between the amount of memory we can use and the quality of the
compression we can achieve. In a third chapter we consider compression in the
read/write streams model, which allows us passes and memory both
polylogarithmic in the size of the input. We first show how to achieve
universal compression using only one pass over one stream. We then show that
one stream is not sufficient for achieving good grammar-based compression.
Finally, we show that two streams are necessary and sufficient for achieving
entropy-only bounds.Comment: draft of PhD thesi
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
Space and Time Efficient Parallel Graph Decomposition, Clustering, and Diameter Approximation
We develop a novel parallel decomposition strategy for unweighted, undirected
graphs, based on growing disjoint connected clusters from batches of centers
progressively selected from yet uncovered nodes. With respect to similar
previous decompositions, our strategy exercises a tighter control on both the
number of clusters and their maximum radius.
We present two important applications of our parallel graph decomposition:
(1) -center clustering approximation; and (2) diameter approximation. In
both cases, we obtain algorithms which feature a polylogarithmic approximation
factor and are amenable to a distributed implementation that is geared for
massive (long-diameter) graphs. The total space needed for the computation is
linear in the problem size, and the parallel depth is substantially sublinear
in the diameter for graphs with low doubling dimension. To the best of our
knowledge, ours are the first parallel approximations for these problems which
achieve sub-diameter parallel time, for a relevant class of graphs, using only
linear space. Besides the theoretical guarantees, our algorithms allow for a
very simple implementation on clustered architectures: we report on extensive
experiments which demonstrate their effectiveness and efficiency on large
graphs as compared to alternative known approaches.Comment: 14 page
Equivalence Classes and Conditional Hardness in Massively Parallel Computations
The Massively Parallel Computation (MPC) model serves as a common abstraction of many modern large-scale data processing frameworks, and has been receiving increasingly more attention over the past few years, especially in the context of classical graph problems. So far, the only way to argue lower bounds for this model is to condition on conjectures about the hardness of some specific problems, such as graph connectivity on promise graphs that are either one cycle or two cycles, usually called the one cycle vs. two cycles problem. This is unlike the traditional arguments based on conjectures about complexity classes (e.g., P ? NP), which are often more robust in the sense that refuting them would lead to groundbreaking algorithms for a whole bunch of problems.
In this paper we present connections between problems and classes of problems that allow the latter type of arguments. These connections concern the class of problems solvable in a sublogarithmic amount of rounds in the MPC model, denoted by MPC(o(log N)), and some standard classes concerning space complexity, namely L and NL, and suggest conjectures that are robust in the sense that refuting them would lead to many surprisingly fast new algorithms in the MPC model. We also obtain new conditional lower bounds, and prove new reductions and equivalences between problems in the MPC model
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