9,129 research outputs found
Darwinian Data Structure Selection
Data structure selection and tuning is laborious but can vastly improve an
application's performance and memory footprint. Some data structures share a
common interface and enjoy multiple implementations. We call them Darwinian
Data Structures (DDS), since we can subject their implementations to survival
of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
search-based optimisation framework that automatically finds optimal, tuned DDS
modulo a test suite, then changes an application to use that DDS. ARTEMIS
achieves substantial performance improvements for \emph{every} project in
Java projects from DaCapo benchmark, popular projects and uniformly
sampled projects from GitHub. For execution time, CPU usage, and memory
consumption, ARTEMIS finds at least one solution that improves \emph{all}
measures for () of the projects. The median improvement across
the best solutions is , , for runtime, memory and CPU
usage.
These aggregate results understate ARTEMIS's potential impact. Some of the
benchmarks it improves are libraries or utility functions. Two examples are
gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
transformation tool. ARTEMIS improves gson by \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
Properties of iterative Monte Carlo single histogram reweighting
We present iterative Monte Carlo algorithm for which the temperature variable
is attracted by a critical point. The algorithm combines techniques of single
histogram reweighting and linear filtering. The 2d Ising model of ferromagnet
is studied numerically as an illustration. In that case, the iterations
uncovered stationary regime with invariant probability distribution function of
temperature which is peaked nearly the pseudocritical temperature of specific
heat. The sequence of generated temperatures is analyzed in terms of stochastic
autoregressive model. The error of histogram reweighting can be better
understood within the suggested model. The presented model yields a simple
relation, connecting variance of pseudocritical temperature and parameter of
linear filtering.Comment: 3 figure
Critical behavior of colloid-polymer mixtures in random porous media
We show that the critical behavior of a colloid-polymer mixture inside a
random porous matrix of quenched hard spheres belongs to the universality class
of the random-field Ising model. We also demonstrate that random-field effects
in colloid-polymer mixtures are surprisingly strong. This makes these systems
attractive candidates to study random-field behavior experimentally.Comment: 4 pages, 3 figures, to appear in Phys. Rev. Let
Shape Effects of Finite-Size Scaling Functions for Anisotropic Three-Dimensional Ising Models
The finite-size scaling functions for anisotropic three-dimensional Ising
models of size (: anisotropy parameter) are
studied by Monte Carlo simulations. We study the dependence of finite-size
scaling functions of the Binder parameter and the magnetization
distribution function . We have shown that the finite-size scaling
functions for at the critical temperature change from a two-peak
structure to a single-peak one by increasing or decreasing from 1. We also
study the finite-size scaling near the critical temperature of the layered
square-lattice Ising model, when the systems have a large two-dimensional
anisotropy. We have found the three-dimensional and two-dimensional finite-size
scaling behavior depending on the parameter which is fixed; a unified view of
3D and 2D finite-size scaling behavior has been obtained for the anisotropic 3D
Ising models.Comment: 6 pages including 11 eps figures, RevTeX, to appear in J. Phys.
Vertex dynamics during domain growth in three-state models
Topological aspects of interfaces are studied by comparing quantitatively the
evolving three-color patterns in three different models, such as the
three-state voter, Potts and extended voter models. The statistical analysis of
some geometrical features allows to explore the role of different elementary
processes during distinct coarsening phenomena in the above models.Comment: 4 pages, 5 figures, to be published in PR
Finite-size Scaling and Universality above the Upper Critical Dimensionality
According to renormalization theory, Ising systems above their upper critical
dimensionality d_u = 4 have classical critical behavior and the ratio of
magnetization moments Q = ^2 / has the universal value 0.456947...
However, Monte Carlo simulations of d = 5 Ising models have been reported which
yield strikingly different results, suggesting that the renormalization
scenario is incorrect. We investigate this issue by simulation of a more
general model in which d_u < 4, and a careful analysis of the corrections to
scaling. Our results are in a perfect agreement with the renormalization theory
and provide an explanation of the discrepancy mentioned.Comment: 5 pages RevTeX, 1 PostScript figure. Accepted for publication in
Physical Review Letter
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