2,340 research outputs found
Measuring and Managing Answer Quality for Online Data-Intensive Services
Online data-intensive services parallelize query execution across distributed
software components. Interactive response time is a priority, so online query
executions return answers without waiting for slow running components to
finish. However, data from these slow components could lead to better answers.
We propose Ubora, an approach to measure the effect of slow running components
on the quality of answers. Ubora randomly samples online queries and executes
them twice. The first execution elides data from slow components and provides
fast online answers; the second execution waits for all components to complete.
Ubora uses memoization to speed up mature executions by replaying network
messages exchanged between components. Our systems-level implementation works
for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the
EasyRec Recommendation Engine, and the OpenEphyra question answering system.
Ubora computes answer quality much faster than competing approaches that do not
use memoization. With Ubora, we show that answer quality can and should be used
to guide online admission control. Our adaptive controller processed 37% more
queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor
A fast and well-conditioned spectral method for singular integral equations
We develop a spectral method for solving univariate singular integral
equations over unions of intervals by utilizing Chebyshev and ultraspherical
polynomials to reformulate the equations as almost-banded infinite-dimensional
systems. This is accomplished by utilizing low rank approximations for sparse
representations of the bivariate kernels. The resulting system can be solved in
operations using an adaptive QR factorization, where is
the bandwidth and is the optimal number of unknowns needed to resolve the
true solution. The complexity is reduced to operations by
pre-caching the QR factorization when the same operator is used for multiple
right-hand sides. Stability is proved by showing that the resulting linear
operator can be diagonally preconditioned to be a compact perturbation of the
identity. Applications considered include the Faraday cage, and acoustic
scattering for the Helmholtz and gravity Helmholtz equations, including
spectrally accurate numerical evaluation of the far- and near-field solution.
The Julia software package SingularIntegralEquations.jl implements our method
with a convenient, user-friendly interface
Towards interactive global illumination effects via sequential Monte Carlo adaptation
Journal ArticleThis paper presents a novel method that effectively combines both control variates and importance sampling in a sequential Monte Carlo context while handling general single-bounce global illumination effects. The radiance estimates computed during the rendering process are cached in an adaptive per-pixel structure that defines dynamic predicate functions for both variance reduction techniques and guarantees well-behaved PDFs, yielding continually increasing efficiencies thanks to a marginal computational overhead
Computing Real Roots of Real Polynomials ... and now For Real!
Very recent work introduces an asymptotically fast subdivision algorithm,
denoted ANewDsc, for isolating the real roots of a univariate real polynomial.
The method combines Descartes' Rule of Signs to test intervals for the
existence of roots, Newton iteration to speed up convergence against clusters
of roots, and approximate computation to decrease the required precision. It
achieves record bounds on the worst-case complexity for the considered problem,
matching the complexity of Pan's method for computing all complex roots and
improving upon the complexity of other subdivision methods by several
magnitudes.
In the article at hand, we report on an implementation of ANewDsc on top of
the RS root isolator. RS is a highly efficient realization of the classical
Descartes method and currently serves as the default real root solver in Maple.
We describe crucial design changes within ANewDsc and RS that led to a
high-performance implementation without harming the theoretical complexity of
the underlying algorithm.
With an excerpt of our extensive collection of benchmarks, available online
at http://anewdsc.mpi-inf.mpg.de/, we illustrate that the theoretical gain in
performance of ANewDsc over other subdivision methods also transfers into
practice. These experiments also show that our new implementation outperforms
both RS and mature competitors by magnitudes for notoriously hard instances
with clustered roots. For all other instances, we avoid almost any overhead by
integrating additional optimizations and heuristics.Comment: Accepted for presentation at the 41st International Symposium on
Symbolic and Algebraic Computation (ISSAC), July 19--22, 2016, Waterloo,
Ontario, Canad
Cache policies for cloud-based systems: To keep or not to keep
In this paper, we study cache policies for cloud-based caching. Cloud-based
caching uses cloud storage services such as Amazon S3 as a cache for data items
that would have been recomputed otherwise. Cloud-based caching departs from
classical caching: cloud resources are potentially infinite and only paid when
used, while classical caching relies on a fixed storage capacity and its main
monetary cost comes from the initial investment. To deal with this new context,
we design and evaluate a new caching policy that minimizes the overall cost of
a cloud-based system. The policy takes into account the frequency of
consumption of an item and the cloud cost model. We show that this policy is
easier to operate, that it scales with the demand and that it outperforms
classical policies managing a fixed capacity.Comment: Proceedings of IEEE International Conference on Cloud Computing 2014
(CLOUD 14
Second chance: A hybrid approach for dynamic result caching and prefetching in search engines
Cataloged from PDF version of article.Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some data that is used to construct the final search result page returned to the user. An alternative strategy is to store in the cache only the result document IDs, which take much less space, allowing results of more queries to be cached. These two strategies lead to an interesting trade-off between the hit rate and the average query response latency. In this work, in order to exploit this trade-off, we propose a hybrid result caching strategy where a dynamic result cache is split into two sections: an HTML cache and a docID cache. Moreover, using a realistic cost model, we evaluate the performance of different result prefetching strategies for the proposed hybrid cache and the baseline HTML-only cache. Finally, we propose a machine learning approach to predict singleton queries, which occur only once in the query stream. We show that when the proposed hybrid result caching strategy is coupled with the singleton query predictor, the hit rate is further improved. © 2013 ACM
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