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Title: Real-time prefetching on shared-memory multi-core systems
Author: Garside, Jamie
ISNI:       0000 0004 5370 5805
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2015
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In recent years, there has been a growing trend towards using multi-core processors in real-time systems to cope with the rising computation requirements of real-time tasks. Coupled with this, the rising memory requirements of these tasks pushes demand beyond what can be provided by small, private on-chip caches, requiring the use of larger, slower off-chip memories such as DRAM. Due to the cost, power requirements and complexity of these memories, they are typically shared between all of the tasks within the system. In order for the execution time of these tasks to be bounded, the response time of the memory and the interference from other tasks also needs to be bounded. While there is a great amount of current research on bounding this interference, one popular method is to effectively partition the available memory bandwidth between the processors in the system. Of course, as the number of processors increases, so does the worst-case blocking, and worst-case blocking times quickly increase with the number of processors. It is difficult to further optimise the arbitration scheme; instead, this scaling problem needs to be approached from another angle. Prefetching has previously been shown to improve the execution time of tasks by speculatively issuing memory accesses ahead of time for items which may be useful in the near future, although these prefetchers are typically not used in real-time systems due to their unpredictable nature. Instead, this work presents a framework by which a prefetcher can be safely used alongside a composable memory arbiter, a predictable prefetching scheme, and finally a method by which this predictable prefetcher can be used to improve the worst-case execution time of a running task.
Supervisor: Audsley, Neil Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available