UR Research > Computer Science Department > CS Ph.D. Theses >

Sharing-aware resource management for multicore systems

URL to cite or link to: http://hdl.handle.net/1802/35262

Srikanthan_rochester_0188E_11866.pdf   1.12 MB (No. of downloads : 230)
PDF of dissertation
Thesis (Ph. D.)--University of Rochester. Department of Computer Science, 2019.
Efficient resource management is a growing problem due to the ever increasing scale and complexity of computational systems and the applications that use them. Modern multicore systems offer abundant compute resources to exploit application-level parallelism. The multiple compute cores within a single system share resources such as processor pipelines, caches, interconnects, and memory, presenting opportunities for efficient data sharing and resource utilization. These multicore systems are regularly employed in large clusters and clouds, hosting a multitude of applications and services simultaneously. While a tremendous amount of research has been aimed at solving various problems in offering infrastructure as a service, managing multiple applications and achieving high utilization and efficiency remains a challenge. The quest to increase utilization may result in higher resource contention and correspondingly unpredictable and often significantly degraded performance. Moreover, the degree of data and resource sharing among different cores is nonuniform, and is dependent on the architecture and applications involved. Identifying and enabling the efficient and synergistic sharing of data and resources while also minimizing resource contention and saturation is key to simultaneously achieving higher individual application performance and overall system efficiency. In this dissertation, we argue that it is possible to provide efficient and deterministic performance in both individual and distributed multicore systems using a holistic approach that simultaneously guides application resource acquisition and manages hardware resources and task placement. Our resource management strategy combines information from the execution environment with application-defined quality of service targets to achieve overall system efficiency while meeting individual application progress guarantees. We demonstrate that aggregate information from low overhead hardware performance counters is sufficient to characterize individual application resource demands and bottlenecks specific to the execution environment. Using this information, we develop a hierarchical resource management strategy that can: monitor performance critical architectural resources and control task placement for optimal use of these resources; understand application bottlenecks, scalability, and parallel efficiency to reallocate resources while guaranteeing quality of service; and consolidate the above information from individual machines into a shared state to guide resource reservation in a distributed setting in order to simultaneously achieve higher individual application performance and overall system efficiency by reducing resource contention and saturation.
Contributor(s):
Sharanyan Srikanthan - Author

Sandhya Dwarkadas - Thesis Advisor

Primary Item Type:
Thesis
Identifiers:
Local Call No. AS38.661
Language:
English
Subject Keywords:
Multicore systems; Parallel efficiency; Resource allocation; Resource management
Sponsor - Description:
Futurewei Technologies -
National Science Foundation (NSF) - CSR-1618497; CCF-1016902; CCF-1217920; CNS-1319353
First presented to the public:
8/31/2020
Originally created:
2019
Date will be made available to public:
2020-08-31   
Original Publication Date:
2019
Previously Published By:
University of Rochester
Place Of Publication:
Rochester, N.Y.
Citation:
Extents:
Illustrations - illustrations (some color)
Number of Pages - xvi, 180 pages
License Grantor / Date Granted:
Catherine Barber / 2019-08-29 11:37:29.106 ( View License )
Date Deposited
2019-08-29 11:37:29.106
Date Last Updated
2019-08-29 14:04:22.029
Submitter:
Catherine Barber

Copyright © This item is protected by copyright, with all rights reserved.

All Versions

Thumbnail Name Version Created Date
Sharing-aware resource management for multicore systems1 2019-08-29 11:37:29.106