324,933 research outputs found
Performance Tuning of Database Systems Using a Context-aware Approach
Database system performance problems have a cascading effect into all aspects of an enterprise application. Database vendors and application developers provide guidelines, best practices and even initial database settings for good
performance. But database performance tuning is not a one-off task. Database administrators have to keep a constant eye on the database performance as the tuning work carried out earlier could be invalidated due to multitude of reasons. Before engaging in a performance tuning endeavor, a database administrator must prioritize which tuning tasks to carry out first. This prioritization is done based on which tuning action would yield highest performance benefit. However, this prediction may not always be accurate. Experiment-based performance tuning methodologies have been introduced as an alternative to prediction-based performance tuning approaches. Experimenting on a representative system similar to the production one allows a database administrator to accurately gauge the performance gain for a particular tuning task. In this paper we propose a novel approach to experiment-based performance tuning with the use of a context-aware application model. Using a proof-of-concept implementation we show how it could be used to automate the detection of performance changes, experiment creation and evaluate the performance tuning outcomes for mixed workload types through database configuration parameter changes
Mira: A Framework for Static Performance Analysis
The performance model of an application can pro- vide understanding about its
runtime behavior on particular hardware. Such information can be analyzed by
developers for performance tuning. However, model building and analyzing is
frequently ignored during software development until perfor- mance problems
arise because they require significant expertise and can involve many
time-consuming application runs. In this paper, we propose a fast, accurate,
flexible and user-friendly tool, Mira, for generating performance models by
applying static program analysis, targeting scientific applications running on
supercomputers. We parse both the source code and binary to estimate
performance attributes with better accuracy than considering just source or
just binary code. Because our analysis is static, the target program does not
need to be executed on the target architecture, which enables users to perform
analysis on available machines instead of conducting expensive exper- iments on
potentially expensive resources. Moreover, statically generated models enable
performance prediction on non-existent or unavailable architectures. In
addition to flexibility, because model generation time is significantly reduced
compared to dynamic analysis approaches, our method is suitable for rapid
application performance analysis and improvement. We present several scientific
application validation results to demonstrate the current capabilities of our
approach on small benchmarks and a mini application
Pseudocanalization regime for magnetic dark-field hyperlens
Hyperbolic metamaterials (HMMs) are the cornerstone of the hyperlens, which
brings the superresolution effect from the near-field to the far-field zone.
For effective application of the hyperlens it should operate in so-called
canalization regime, when the phase advancement of the propagating fields is
maximally supressed, and thus field broadening is minimized. For conventional
hyperlenses it is relatively straightforward to achieve canalization by tuning
the anisotropic permittivity tensor. However, for a dark-field hyperlens
designed to image weak scatterers by filtering out background radiation
(dark-field regime) this approach is not viable, because design requirements
for such filtering and elimination of phase advancement i.e. canalization, are
mutually exclusive. Here we propose the use of magnetic (-positive and
negative) HMMs to achieve phase cancellation at the output equivalent to the
performance of a HMM in the canalized regime. The proposed structure offers
additional flexibility over simple HMMs in tuning light propagation. We show
that in this ``pseudocanalizing'' configuration quality of an image is
comparable to a conventional hyperlens, while the desired filtering of the
incident illumination associated with the dark-field hyperlens is preserved
An Automatic Tuning MPC with Application to Ecological Cruise Control
Model predictive control (MPC) is a powerful tool for planning and
controlling dynamical systems due to its capacity for handling constraints and
taking advantage of preview information. Nevertheless, MPC performance is
highly dependent on the choice of cost function tuning parameters. In this
work, we demonstrate an approach for online automatic tuning of an MPC
controller with an example application to an ecological cruise control system
that saves fuel by using a preview of road grade. We solve the global fuel
consumption minimization problem offline using dynamic programming and find the
corresponding MPC cost function by solving the inverse optimization problem. A
neural network fitted to these offline results is used to generate the desired
MPC cost function weight during online operation. The effectiveness of the
proposed approach is verified in simulation for different road geometries
Effective Unsupervised Author Disambiguation with Relative Frequencies
This work addresses the problem of author name homonymy in the Web of
Science. Aiming for an efficient, simple and straightforward solution, we
introduce a novel probabilistic similarity measure for author name
disambiguation based on feature overlap. Using the researcher-ID available for
a subset of the Web of Science, we evaluate the application of this measure in
the context of agglomeratively clustering author mentions. We focus on a
concise evaluation that shows clearly for which problem setups and at which
time during the clustering process our approach works best. In contrast to most
other works in this field, we are sceptical towards the performance of author
name disambiguation methods in general and compare our approach to the trivial
single-cluster baseline. Our results are presented separately for each correct
clustering size as we can explain that, when treating all cases together, the
trivial baseline and more sophisticated approaches are hardly distinguishable
in terms of evaluation results. Our model shows state-of-the-art performance
for all correct clustering sizes without any discriminative training and with
tuning only one convergence parameter.Comment: Proceedings of JCDL 201
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