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@MASTERSTHESIS{Huckebrink:1019304,
author = {Huckebrink, Ben-Jay},
othercontributors = {Müller, Matthias S. and Lankes, Stefan and Klinkenberg,
Jannis},
title = {{E}valuating and comparing data placement optimization
frameworks for heterogeneous memory systems},
school = {RWTH Aachen University},
type = {Bachelorarbeit},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-08320},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Bachelorarbeit, RWTH Aachen University, 2025},
abstract = {The memory-related demands of scientific applications rise
at an ever-accelerating pace. However, traditional dynamic
random access memory (DRAM) has not kept up with these
increasing memory capacity, speed, and energy efficiency
demands. In response, heterogeneous memory systems employing
multiple memory types, such as non-volatile memory (NVM) or
high-bandwidth memory (HBM), alongside DRAM have risen to
prevalence. Leveraging the advantages of such systems
involves placing individual application data structures into
different memory types depending on their memory access
behaviors. Since manually conducting such a placement
optimization requires detailed application knowledge and a
large time investment, previous research developed data
placement optimization frameworks to automate this process
and improve the placement decisions made. However, previous
research on these frameworks has not adequately evaluated
their efficacy. Most existing work tests only the execution
time performance of the frameworks' placement decisions,
leaving the frameworks' user experience and energy
efficiency benefits unquantified. Crucially, existing
research also does not compare the different frameworks
against one another. In combination, these shortcomings
impede research on future frameworks, since the specific
strengths and weaknesses of already existing approaches
remain unknown, meaning their weaknesses cannot be improved
systematically. In this thesis, I address this shortage by
evaluating and comparing three state-of-the-art data
placement optimization frameworks in-depth. For this
purpose, I develop a custom, highly configurable synthetic
benchmark that can systematically alter its memory access
behaviors. This configurability allows me to detail specific
strengths and weaknesses of each framework's placement
optimization algorithm and quantify their impact in terms of
the execution time and energy efficiency the made placement
decisions achieve. By also testing the frameworks on four
proxy applications, I assess the real-world implications of
the identified advantages and disadvantages. Further, using
the proxy applications, I uncover shortcomings in the
frameworks' user experience. Based on my observations, I
propose modifications to the frameworks to improve their
decision-making and their user experience.},
cin = {123010 / 022000 / 120000},
ddc = {004},
cid = {$I:(DE-82)123010_20140620$ / $I:(DE-82)022000_20140101$ /
$I:(DE-82)120000_20140620$},
typ = {PUB:(DE-HGF)2},
doi = {10.18154/RWTH-2025-08320},
url = {https://publications.rwth-aachen.de/record/1019304},
}
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