14 research outputs found
Performance Modeling and Analysis of a Massively Parallel DIRECTâ Part 1
Modeling and analysis techniques are used to investigate
the performance of a massively parallel version
of DIRECT, a global search algorithm widely used
in multidisciplinary design optimization applications.
Several highdimensional
benchmark functions and
real world problems are used to test the design effectiveness
under various problem structures. Theoretical
and experimental results are compared for two
parallel clusters with different system scale and network
connectivity. The present work aims at studying
the performance sensitivity to important parameters
for problem configurations, parallel schemes,
and system settings. The performance metrics
include the memory usage, load balancing, parallel
efficiency, and scalability. An analytical bounding
model is constructed to measure the load balancing
performance under different schemes. Additionally,
linear regression models are used to characterize
two major overhead sourcesâinterprocessor communication
and processor idleness, and also applied
to the isoefficiency functions in scalability analysis.
For a variety of highdimensional
problems and large
scale systems, the massively parallel design has
achieved reasonable performance. The results of
the performance study provide guidance for efficient
problem and scheme configuration. More importantly,
the generalized design considerations and
analysis techniques are beneficial for transforming
many global search algorithms to become effective
large scale parallel optimization tools
Performance Modeling and Analysis of a Massively Parallel DIRECTâ Part 2
Modeling and analysis techniques are used to investigate
the performance of a massively parallel version
of DIRECT, a global search algorithm widely used
in multidisciplinary design optimization applications.
Several highdimensional
benchmark functions and
real world problems are used to test the design
effectiveness under various problem structures. In
this second part of a twopart
work, theoretical and
experimental results are compared for two parallel
clusters with different system scale and network
connectivity. The first part studied performance
sensitivity to important parameters for problem configurations
and parallel schemes, using performance
metrics such as memory usage, load balancing,
and parallel efficiency. Here linear regression models
are used to characterize two major overhead
sourcesâinterprocessor communication and processor
idlenessâand also applied to the isoefficiency
functions in scalability analysis. For a variety of
highdimensional
problems and large scale systems,
the massively parallel design has achieved reasonable
performance. The results of the performance
study provide guidance for efficient problem and
scheme configuration. More importantly, the design
considerations and analysis techniques generalize to
the transformation of other global search algorithms
into effective large scale parallel optimization tools
Design and Implementation of a Massively Parallel Version of DIRECT
This paper describes several massively parallel implementations for a global search algorithm DIRECT.
Two parallel schemes take different approaches to address DIRECT's design challenges imposed by memory requirements
and data dependency. Three design aspects in topology, data structures, and task allocation are compared in
detail. The goal is to analytically investigate the strengths and weaknesses of these parallel schemes, identify several
key sources of inefficiency, and experimentally evaluate a number of improvements in the latest parallel DIRECT
implementation. The performance studies demonstrate improved data structure efficiency and load balancing on a
2200 processor cluster
Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations
This paper presents a statistical framework for assessing wireless systems
performance using hierarchical data mining techniques. We consider WCDMA
(wideband code division multiple access) systems with two-branch STTD (space
time transmit diversity) and 1/2 rate convolutional coding (forward error
correction codes). Monte Carlo simulation estimates the bit error probability
(BEP) of the system across a wide range of signal-to-noise ratios (SNRs). A
performance database of simulation runs is collected over a targeted space of
system configurations. This database is then mined to obtain regions of the
configuration space that exhibit acceptable average performance. The shape of
the mined regions illustrates the joint influence of configuration parameters
on system performance. The role of data mining in this application is to
provide explainable and statistically valid design conclusions. The research
issue is to define statistically meaningful aggregation of data in a manner
that permits efficient and effective data mining algorithms. We achieve a good
compromise between these goals and help establish the applicability of data
mining for characterizing wireless systems performance
BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)
We describe a binding schema markup language (BSML) for describing data
interchange between scientific codes. Such a facility is an important
constituent of scientific problem solving environments (PSEs). BSML is designed
to integrate with a PSE or application composition system that views model
specification and execution as a problem of managing semistructured data. The
data interchange problem is addressed by three techniques for processing
semistructured data: validation, binding, and conversion. We present BSML and
describe its application to a PSE for wireless communications system design
On the Shoulders of Giants: The Growing Impact of Older Articles
In this paper, we examine the evolution of the impact of older scholarly
articles. We attempt to answer four questions. First, how often are older
articles cited and how has this changed over time. Second, how does the impact
of older articles vary across different research fields. Third, is the change
in the impact of older articles accelerating or slowing down. Fourth, are these
trends different for much older articles.
To answer these questions, we studied citations from articles published in
1990-2013. We computed the fraction of citations to older articles from
articles published each year as the measure of impact. We considered articles
that were published at least 10 years before the citing article as older
articles. We computed these numbers for 261 subject categories and 9 broad
areas of research. Finally, we repeated the computation for two other
definitions of older articles, 15 years and older and 20 years and older.
There are three conclusions from our study. First, the impact of older
articles has grown substantially over 1990-2013. In 2013, 36% of citations were
to articles that are at least 10 years old; this fraction has grown 28% since
1990. The fraction of older citations increased over 1990-2013 for 7 out of 9
broad areas and 231 out of 261 subject categories. Second, the increase over
the second half (2002-2013) was double the increase in the first half
(1990-2001).
Third, the trend of a growing impact of older articles also holds for even
older articles. In 2013, 21% of citations were to articles >= 15 years old with
an increase of 30% since 1990 and 13% of citations were to articles >= 20 years
old with an increase of 36%.
Now that finding and reading relevant older articles is about as easy as
finding and reading recently published articles, significant advances aren't
getting lost on the shelves and are influencing work worldwide for years after
Dynamic Data Structures for a Direct Search Algorithm
The DIRECT (DIviding RECTangles) algorithm of Jones, Perttunen, and Stuckman (1993), a variant of Lipschitzian methods for bound constrained global optimization, has proved effective even in higher dimensions. However, the performance of a DIRECT implementation in real applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice, since it is difficult to predict memory resource requirements in advance. This is especially critical in multidisciplinary engineering design applications, where the DIRECT optimization is just one small component of a much larger computation, and any component failure aborts the entire design process. To make the DIRECT global optimization algorithm efficient and robust on large-scale, multidisciplinary engineering problems, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus of this paper is on design issues of the dynamic data structures, and related memory management strategies. Numerical computing techniques and modiïŹcations of Jonesâ original DIRECT algorithm in terms of stopping rules and box selection rules are also explored. Performance studies are done for synthetic test problems with multiple local optima. Results for application to a site-specific system simulator for wireless communications systems (S4W) are also presented to demonstrate the effectiveness of the proposed dynamic data structures for an implementation of DIRECT