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Addressing parallel application memory consumption
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Perks, O. F. J. (2013) Addressing parallel application memory consumption. PhD thesis, University of Warwick.
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WRAP_THESIS_Perks_2013.pdf - Submitted Version Download (4Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b2692675~S9
Abstract
Recent trends in computer architecture are furthering the gap between CPU
capabilities and those of the memory system. The rise of multi-core processors
is having a dramatic effect on memory interactions, not just with respect to
performance but crucially to capacity. The slow growth of DRAM capacity,
coupled with configuration limitations, is driving up the cost of memory systems
as a proportion of total HPC platform cost.
As a result, scientific institutions are increasingly interested in application
memory consumption, and in justifying the cost associated with maintaining
high memory-per-core ratios. By studying the scaling behaviour of applications,
both in terms of runtime and memory consumption, we are able to demonstrate
a decrease in workload efficiency in low memory environments, resulting from
poor memory scalability.
Current tools are lacking in performance and analytical capabilities motivating
the development of a new suite of tools for capturing and analysing memory
consumption in large scale parallel applications.
By observing and analysing memory allocations we are able to record not
only how much but more crucially where and when an application uses its memory.
We use use this analysis to look at some of the key principles in application
scaling such as processor decomposition, parallelisation models and runtime
libraries, and their associated effects on memory consumption. We demonstrate
how the data storage model of OpenMPI implementations inherently prevents
scaling due to memory requirements, and investigate the benefits of different
solutions.
Finally, we show how by analysing information gathered during application
execution we can automatically generate models to predict application memory
consumption, at different scale and runtime configurations. In addition we predict, using these models, how implementation changes could affect the memory
consumption of an industry strength benchmark.
Item Type: | Thesis (PhD) |
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
Library of Congress Subject Headings (LCSH): | Computer architecture, Parallel processing (Electronic computers), Application software, Memory management (Computer science) |
Official Date: | July 2013 |
Institution: | University of Warwick |
Theses Department: | Department of Computer Science |
Thesis Type: | PhD |
Publication Status: | Unpublished |
Supervisor(s)/Advisor: | Jarvis, Stephen A., 1970- |
Extent: | xxi, 163 leaves : charts. |
Language: | eng |
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