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Profiling Methods for Memory Centric Software Performance Analysis
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computer Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (UART)
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To reduce latency and increase bandwidth to memory, modern microprocessors are often designed with deep memory hierarchies including several levels of caches. For such microprocessors, both the latency and the bandwidth to off-chip memory are typically about two orders of magnitude worse than the latency and bandwidth to the fastest on-chip cache. Consequently, the performance of many applications is largely determined by how well they utilize the caches and bandwidths in the memory hierarchy. For such applications, there are two principal approaches to improve performance: optimize the memory hierarchy and optimize the software. In both cases, it is important to both qualitatively and quantitatively understand how the software utilizes and interacts with the resources (e.g., cache and bandwidths) in the memory hierarchy.

This thesis presents several novel profiling methods for memory-centric software performance analysis. The goal of these profiling methods is to provide general, high-level, quantitative information describing how the profiled applications utilize the resources in the memory hierarchy, and thereby help software and hardware developers identify opportunities for memory related hardware and software optimizations. For such techniques to be broadly applicable the data collection should have minimal impact on the profiled application, while not being dependent on custom hardware and/or operating system extensions. Furthermore, the resulting profiling information should be accurate and easy to interpret.

While several use cases are presented, the main focus of this thesis is the design and evaluation of the core profiling methods. These core profiling methods measure and/or estimate how high-level performance metrics, such as miss-and fetch ratio; off-chip bandwidth demand; and execution rate are affected by the amount of resources the profiled applications receive. This thesis shows that such high-level profiling information can be accurately obtained with very little impact on the profiled applications and without requiring costly simulations or custom hardware support.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. , 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1000
National Category
Computer Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-182594ISBN: 978-91-554-8541-2 (print)OAI: oai:DiVA.org:uu-182594DiVA: diva2:560132
Public defence
2012-12-21, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:00 (English)
Opponent
Supervisors
Projects
UPMARC
Available from: 2012-11-29 Created: 2012-10-11 Last updated: 2014-07-21Bibliographically approved
List of papers
1. StatStack: Efficient modeling of LRU caches
Open this publication in new window or tab >>StatStack: Efficient modeling of LRU caches
2010 (English)In: Proc. International Symposium on Performance Analysis of Systems and Software: ISPASS 2010, Piscataway, NJ: IEEE , 2010, 55-65 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2010
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-136247 (URN)10.1109/ISPASS.2010.5452069 (DOI)978-1-4244-6023-6 (ISBN)
Projects
Coder-mpUPMARC
Available from: 2010-04-19 Created: 2010-12-10 Last updated: 2013-02-11Bibliographically approved
2. Fast modeling of shared caches in multicore systems
Open this publication in new window or tab >>Fast modeling of shared caches in multicore systems
2011 (English)In: Proc. 6th International Conference on High Performance and Embedded Architectures and Compilers, New York: ACM Press , 2011, 147-157 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
New York: ACM Press, 2011
National Category
Computer Science
Identifiers
urn:nbn:se:uu:diva-146757 (URN)10.1145/1944862.1944885 (DOI)978-1-4503-0241-8 (ISBN)
Projects
Coder-mpUPMARC
Available from: 2011-02-20 Created: 2011-02-20 Last updated: 2013-02-11Bibliographically approved
3. Cache Pirating: Measuring the Curse of the Shared Cache
Open this publication in new window or tab >>Cache Pirating: Measuring the Curse of the Shared Cache
2011 (English)In: Proc. 40th International Conference on Parallel Processing, IEEE Computer Society, 2011, 165-175 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2011
National Category
Computer Engineering
Identifiers
urn:nbn:se:uu:diva-181254 (URN)10.1109/ICPP.2011.15 (DOI)978-1-4577-1336-1 (ISBN)
Conference
ICPP 2011
Projects
UPMARCCoDeR-MP
Available from: 2011-10-17 Created: 2012-09-20 Last updated: 2013-02-11Bibliographically approved
4. Quantitative Characterization of Memory Contention
Open this publication in new window or tab >>Quantitative Characterization of Memory Contention
2012 (English)Report (Other academic)
Abstract [en]

On multicore processors, co-executing applications compete for shared resources, such as cache capacity and memory bandwidth. This leads to suboptimal resource allocation and can cause substantial performance loss, which makes it important to effectively manage these shared resources. This, however, requires insights into how the applications are impacted by such resource sharing.

While there are several methods to analyze the performance impact of cache contention, less attention has been paid to general, quantitative methods for analyzing the impact of contention for memory bandwidth. To this end we introduce the Bandwidth Bandit, a general, quantitative, profiling method for analyzing the performance impact of contention for memory bandwidth on multicore machines.

The profiling data captured by the Bandwidth Bandit is presented in a it bandwidth graph. This graph accurately captures the measured application's performance as a function of its available memory bandwidth, and enables us to determine how much the application suffers when its available bandwidth is reduced. To demonstrate the value of this data, we present a case study in which we use the bandwidth graph to analyze the performance impact of memory contention when co-running multiple instances of single threaded application.

Place, publisher, year, edition, pages
Uppsala: Uppsala universitet, 2012. 10 p.
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2012-029
National Category
Computer Systems
Research subject
Computer Systems Sciences
Identifiers
urn:nbn:se:uu:diva-182445 (URN)
Available from: 2013-03-28 Created: 2012-10-10 Last updated: 2013-03-28Bibliographically approved
5. A Profiling Method for Analyzing Scalability Bottlenecks on Multicores
Open this publication in new window or tab >>A Profiling Method for Analyzing Scalability Bottlenecks on Multicores
2012 (English)Report (Other academic)
Publisher
12 p.
National Category
Computer Systems
Identifiers
urn:nbn:se:uu:diva-182453 (URN)
Available from: 2012-10-10 Created: 2012-10-10 Last updated: 2013-02-11Bibliographically approved

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