11,435 research outputs found
Optimizing I/O for Big Array Analytics
Big array analytics is becoming indispensable in answering important
scientific and business questions. Most analysis tasks consist of multiple
steps, each making one or multiple passes over the arrays to be analyzed and
generating intermediate results. In the big data setting, I/O optimization is a
key to efficient analytics. In this paper, we develop a framework and
techniques for capturing a broad range of analysis tasks expressible in
nested-loop forms, representing them in a declarative way, and optimizing their
I/O by identifying sharing opportunities. Experiment results show that our
optimizer is capable of finding execution plans that exploit nontrivial I/O
sharing opportunities with significant savings.Comment: VLDB201
Wireless Scheduling with Power Control
We consider the scheduling of arbitrary wireless links in the physical model
of interference to minimize the time for satisfying all requests. We study here
the combined problem of scheduling and power control, where we seek both an
assignment of power settings and a partition of the links so that each set
satisfies the signal-to-interference-plus-noise (SINR) constraints.
We give an algorithm that attains an approximation ratio of , where is the number of links and is the ratio
between the longest and the shortest link length. Under the natural assumption
that lengths are represented in binary, this gives the first approximation
ratio that is polylogarithmic in the size of the input. The algorithm has the
desirable property of using an oblivious power assignment, where the power
assigned to a sender depends only on the length of the link. We give evidence
that this dependence on is unavoidable, showing that any
reasonably-behaving oblivious power assignment results in a -approximation.
These results hold also for the (weighted) capacity problem of finding a
maximum (weighted) subset of links that can be scheduled in a single time slot.
In addition, we obtain improved approximation for a bidirectional variant of
the scheduling problem, give partial answers to questions about the utility of
graphs for modeling physical interference, and generalize the setting from the
standard 2-dimensional Euclidean plane to doubling metrics. Finally, we explore
the utility of graph models in capturing wireless interference.Comment: Revised full versio
Hypernode reduction modulo scheduling
Software pipelining is a loop scheduling technique that extracts parallelism from loops by overlapping the execution of several consecutive iterations. Most prior scheduling research has focused on achieving minimum execution time, without regarding register requirements. Most strategies tend to stretch operand lifetimes because they schedule some operations too early or too late. The paper presents a novel strategy that simultaneously schedules some operations late and other operations early, minimizing all the stretchable dependencies and therefore reducing the registers required by the loop. The key of this strategy is a pre-ordering that selects the order in which the operations will be scheduled. The results show that the method described in this paper performs better than other heuristic methods and almost as well as a linear programming method but requiring much less time to produce the schedules.Peer ReviewedPostprint (published version
Improving Table Compression with Combinatorial Optimization
We study the problem of compressing massive tables within the
partition-training paradigm introduced by Buchsbaum et al. [SODA'00], in which
a table is partitioned by an off-line training procedure into disjoint
intervals of columns, each of which is compressed separately by a standard,
on-line compressor like gzip. We provide a new theory that unifies previous
experimental observations on partitioning and heuristic observations on column
permutation, all of which are used to improve compression rates. Based on the
theory, we devise the first on-line training algorithms for table compression,
which can be applied to individual files, not just continuously operating
sources; and also a new, off-line training algorithm, based on a link to the
asymmetric traveling salesman problem, which improves on prior work by
rearranging columns prior to partitioning. We demonstrate these results
experimentally. On various test files, the on-line algorithms provide 35-55%
improvement over gzip with negligible slowdown; the off-line reordering
provides up to 20% further improvement over partitioning alone. We also show
that a variation of the table compression problem is MAX-SNP hard.Comment: 22 pages, 2 figures, 5 tables, 23 references. Extended abstract
appears in Proc. 13th ACM-SIAM SODA, pp. 213-222, 200
Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method for collecting data for which the least squares
estimator for the ranking problem has maximal Fisher information. Our approach,
based on experimental design, is to view data collection as a bi-level
optimization problem where the inner problem is the ranking problem and the
outer problem is to identify data which maximizes the informativeness of the
ranking. Under certain assumptions, the data collection problem decouples,
reducing to a problem of finding multigraphs with large algebraic connectivity.
This reduction of the data collection problem to graph-theoretic questions is
one of the primary contributions of this work. As an application, we study the
Yahoo! Movie user rating dataset and demonstrate that the addition of a small
number of well-chosen pairwise comparisons can significantly increase the
Fisher informativeness of the ranking. As another application, we study the
2011-12 NCAA football schedule and propose schedules with the same number of
games which are significantly more informative. Using spectral clustering
methods to identify highly-connected communities within the division, we argue
that the NCAA could improve its notoriously poor rankings by simply scheduling
more out-of-conference games.Comment: 31 pages, 10 figures, 3 table
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