St Andrews Research Repository

St Andrews University Home
View Item 
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  •   St Andrews Research Repository
  • University of St Andrews Research
  • University of St Andrews Research
  • University of St Andrews Research
  • View Item
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Learning-based dynamic pinning of parallelized applications in many-core systems

Thumbnail
View/Open
2019_PDP19_Learning_based_Dynamic_Pinning.pdf (571.3Kb)
Date
21/03/2019
Author
Chasparis, Georgios
Rossbory, Michael
Janjic, Vladimir
Hammond, Kevin
Keywords
QA75 Electronic computers. Computer science
NDAS
Metadata
Show full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
Abstract
This paper introduces a learning-based framework for dynamic placement of threads of parallel applications to the cores of Non-Uniform Memory Access (NUMA) architectures. Adaptation takes place in two levels, where at the first level each thread independently decides on which group of cores (NUMA node) it will execute, and on the second level it decides to which particular core from the group it will be pinned. Naturally, these two adaptation levels run on different time-scales: a low-frequency switching for the NUMA-node adaptation, and a high-frequency switching for the CPU-node level adaptation. In addition, the learning dynamics have been designed to handle measurement noise and rapid variations in the performance of the threads. The advantage of the proposed learning scheme is the ability to easily incorporate any multi-objective criterion and easily adapt to performance variations during runtime. Our objective is to demonstrate that this framework is appropriate for supervising parallel processes and intervening with respect to better resource allocation. Under the multi-objective criterion of maximizing total completed instructions per second (i.e., both computational and memory-access instructions), we compare the performance of the proposed scheme with the Linux operating system scheduler. We have observed that performance improvement could be significant especially under limited availability of resources and under irregular memory-access patterns.
Citation
Chasparis , G , Rossbory , M , Janjic , V & Hammond , K 2019 , Learning-based dynamic pinning of parallelized applications in many-core systems . in Proceedings 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2019) . , 8671569 , Institute of Electrical and Electronics Engineers Inc. , 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) , Pavia , Italy , 13/02/19 . https://doi.org/10.1109/EMPDP.2019.8671569
 
conference
 
Publication
Proceedings 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2019)
DOI
https://doi.org/10.1109/EMPDP.2019.8671569
Type
Conference item
Rights
© 2019, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/PDP.2019.00010
Description
Funding: This work has been supported by the European Union grant EU H2020-ICT-2014-1 project RePhrase (No. 644235). It has also been partially supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.
Collections
  • University of St Andrews Research
URL
https://arxiv.org/abs/1803.00355
URI
http://hdl.handle.net/10023/18053

Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

Advanced Search

Browse

All of RepositoryCommunities & CollectionsBy Issue DateNamesTitlesSubjectsClassificationTypeFunderThis CollectionBy Issue DateNamesTitlesSubjectsClassificationTypeFunder

My Account

Login

Open Access

To find out how you can benefit from open access to research, see our library web pages and Open Access blog. For open access help contact: openaccess@st-andrews.ac.uk.

Accessibility

Read our Accessibility statement.

How to submit research papers

The full text of research papers can be submitted to the repository via Pure, the University's research information system. For help see our guide: How to deposit in Pure.

Electronic thesis deposit

Help with deposit.

Repository help

For repository help contact: Digital-Repository@st-andrews.ac.uk.

Give Feedback

Cookie policy

This site may use cookies. Please see Terms and Conditions.

Usage statistics

COUNTER-compliant statistics on downloads from the repository are available from the IRUS-UK Service. Contact us for information.

© University of St Andrews Library

University of St Andrews is a charity registered in Scotland, No SC013532.

  • Facebook
  • Twitter