Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Experimental Verification and Analysis of Dynamic Loop Scheduling in
  Scientific Applications   Scientific applications are often irregular and characterized by large
computationally-intensive parallel loops. Dynamic loop scheduling (DLS)
techniques improve the performance of computationally-intensive scientific
applications via load balancing of their execution on high-performance
computing (HPC) systems. Identifying the most suitable choices of data
distribution strategies, system sizes, and DLS techniques which improve the
performance of a given application, requires intensive assessment and a large
number of exploratory native experiments (using real applications on real
systems), which may not always be feasible or practical due to associated time
and costs. In such cases, simulative experiments are more appropriate for
studying the performance of applications. This motivates the question of How
realistic are the simulations of executions of scientific applications using
DLS on HPC platforms? In the present work, a methodology is devised to answer
this question. It involves the experimental verification and analysis of the
performance of DLS in scientific applications. The proposed methodology is
employed for a computer vision application executing using four DLS techniques
on two different HPC plat- forms, both via native and simulative experiments.
The evaluation and analysis of the native and simulative results indicate that
the accuracy of the simulative experiments is strongly influenced by the
approach used to extract the computational effort of the application (FLOP- or
time-based), the choice of application model representation into simulation
(data or task parallel), and the available HPC subsystem models in the
simulator (multi-core CPUs, memory hierarchy, and network topology). The
minimum and the maximum percent errors achieved between the native and the
simulative experiments are 0.95% and 8.03%, respectively.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg