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Analyzing performance variation of task schedulers with TaskInsight
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team)ORCID iD: 0000-0003-2314-7307
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication. (Uppsala Architecture Research Team)
2018 (English)In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 75, p. 11-27Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2018. Vol. 75, p. 11-27
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:uu:diva-340202DOI: 10.1016/j.parco.2018.02.003OAI: oai:DiVA.org:uu-340202DiVA, id: diva2:1178003
Projects
UPMARCResource Sharing Modeling
Available from: 2018-02-22 Created: 2018-01-26 Last updated: 2018-03-03Bibliographically approved
In thesis
1. Modeling the interactions between tasks and the memory system
Open this publication in new window or tab >>Modeling the interactions between tasks and the memory system
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Making computer systems more energy efficient while obtaining the maximum performance possible is key for future developments in engineering, medicine, entertainment, etc. However it has become a difficult task due to the increasing complexity of hardware and software, and their interactions. For example, developers have to deal with deep, multi-level cache hierarchies on modern CPUs, and keep busy thousands of cores in GPUs, which makes the programming process more difficult.

To simplify this task, new abstractions and programming models are becoming popular. Their goal is to make applications more scalable and efficient, while still providing the flexibility and portability of old, widely adopted models. One example of this is task-based programming, where simple independent tasks (functions) are delegated to a runtime system which orchestrates their execution. This approach has been successful because the runtime can automatically distribute work across hardware cores and has the potential to minimize data movement and placement (e.g., being aware of the cache hierarchy).

To build better runtime systems, it is crucial to understand bottlenecks in the performance of current and future multicore systems. In this thesis, we provide fast, accurate and mathematically-sound models and techniques to understand the execution of task-based applications concerning three key aspects: memory behavior (data locality), scheduling, and performance. With these methods, we lay the groundwork for improving runtime system, providing insight into the interplay between the schedule's behavior, data reuse through the cache hierarchy, and the resulting performance.

Place, publisher, year, edition, pages
Uppsala University, 2017
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2017-002
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-335530 (URN)
Supervisors
Projects
UPMARC
Available from: 2017-10-01 Created: 2017-12-06 Last updated: 2018-01-31Bibliographically approved

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Ceballos, GermánHugo, AndraBlack-Schaffer, David

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