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Predicting Application Performance for Chip Multiprocessors

Moore, Ryan (2014) Predicting Application Performance for Chip Multiprocessors. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Today's computers have processors with multiple cores that allow several applications to execute simultaneously. The way resources are allocated to an application affects whether performance objectives, such as quality of service (QoS), are satisfied. To ensure objectives are met, resources must be carefully but quickly allocated in response to changing runtime conditions.

Traditional approaches to resource allocation take place either purely online or offline. Online methods do not scale to large, multiple core systems because there are too many allocations to evaluate at runtime. Offline methods cannot handle unanticipated workloads or changes. A hybrid approach could combine the lower runtime overhead of offline approaches with the flexibility of online approaches.

This thesis introduces AUTO, a hybrid solution to perform resource allocation. AUTO dynamically adjusts thread count, core count, and core type. It does so in accordance with a user-provided policy to meet performance objectives. AUTO's capabilities come from four prediction techniques. The first technique builds and uses models that consider CPU contention and application scalability in order to select co-running applications' thread counts. The second technique predicts applications' preferred thread-to-core mappings. The predictions are thread count independent and are translated into concrete thread-to-core mappings based on resource availability. The third technique predicts application performance under thread-to-core mappings. The final technique selects thread count and core count for applications on a system with cores of different capabilities.

AUTO was tested in several scenarios. In each scenario, it was shown to be an effective, efficient solution to resource allocation. First, it was used to select the thread count of one or more co-running applications. Second, it was used to select application thread-to-core mappings. Third, it was used to make predictions about application performance under thread-to-core mappings. Finally, it was used to select both thread count and core type for applications on a computer with cores of different capabilities. AUTO's resource allocation and models allow for more effective and more efficient policies. By using hybrid online and offline techniques, AUTO solves the problem of allocating threads and cores to meet performance objectives.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Moore, Ryanrym4@pitt.eduRYM4
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChilders, Bruce Rchilders@cs.pitt.eduCHILDERS
Committee MemberCho, Sangyeuncho@cs.pitt.eduSANGYEUN
Committee MemberKandemir,
Committee MemberZhang, Youtaozhangyt@cs.pitt.eduYOUTAO
Date: 31 January 2014
Date Type: Publication
Defense Date: 21 November 2013
Approval Date: 31 January 2014
Submission Date: 5 December 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 163
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: computer architecture, multiprocessors, performance prediction, multithreaded
Date Deposited: 31 Jan 2014 21:12
Last Modified: 15 Nov 2016 14:16

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