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@PHDTHESIS{Wienke:711110,
author = {Wienke, Sandra Juliane},
othercontributors = {Müller, Matthias S. and Ludwig, Thomas},
title = {{P}roductivity and {S}oftware {D}evelopment {E}ffort
{E}stimation in {H}igh-{P}erformance {C}omputing; 1.
{A}uflage},
volume = {9},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {Apprimus Verlag},
reportid = {RWTH-2017-10649},
isbn = {978-3-86359-572-2},
series = {Ergebnisse aus der Informatik},
pages = {1 Online-Ressource (xvi, 226 Seiten) : Illustrationen,
Diagramme},
year = {2017},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2018; Dissertation, RWTH Aachen University, 2017},
abstract = {Ever increasing demands for computational power are
concomitant with rising electrical power needs and
complexity in hardware and software designs. According
increasing expenses for hardware, electrical power and
programming tighten the rein on available budgets. Hence, an
informed decision making on how to invest available budgets
is more important than ever. Especially for procurements, a
quantitative metric is needed to predict the cost
effectiveness of an HPC center.In this work, I set up models
and methodologies to support the HPC procurement process of
German HPC centers. I model cost effectiveness as a
productivity figure of merit of HPC centers by defining a
ratio of scientific outcome generated over the lifetime of
the HPC system to its total costs of ownership (TCO). I
further define scientific outcome as number of
scientific-application runs to embrace the multi-job nature
of an HPC system in a meaningful way. I investigate the
predictability of the productivity model's parameters and
show their robustnesstowards errors in various real-world
HPC setups. Case studies further verify the model's
applicability, e.g., to compare hardware setups or optimize
system lifetime.I continue to investigate total ownership
costs of HPC centers as part of the productivity metric. I
model TCO by splitting expenses into one-time and annual
costs, node-based and node-type-based costs, as well as,
system-dependent and application-dependent costs.
Furthermore, I discuss quantification and predictability
capabilities of all TCO components.I tackle the challenge of
estimating HPC software development effort as TCO component
with increasing importance. For that, I establish a
methodology that is based on a so-called performance
life-cycle describing the relationship of effort to
performance achieved by spending the respective effort. To
identify further impactfactors on application development
effort, I apply ranking surveys that reveal priorities for
quantifying effects. Such an effect is the developer's
pre-knowledge in HPC whose quantification is addressed by
confidence ratings in so-called knowledge surveys. I also
examine the quantification of impacts of the parallel
programming model by proposing a pattern-based approach.
Since meaningful quantifications rely on sufficient and
appropriate data sets, I broaden previous human-subject
based data collections by introducing tools and methods for
a community effort. Finally, I present the applicability of
my methodologies and models in a case study that covers a
real-world application from aeroacoustics simulation.},
cin = {120000 / 123010},
ddc = {004},
cid = {$I:(DE-82)120000_20140620$ / $I:(DE-82)123010_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2017-10649},
url = {https://publications.rwth-aachen.de/record/711110},
}
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