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Thursday, September 24 • 13:40 - 14:00
Kernel Techniques to Optimize Memory Bandwidth with Predictable Latency

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Consolidating multiple applications on the same multi-core platform while preserving their performance is of the utmost importance in real-time systems. A similar problem has emerged in cloud computing systems. Meeting SLAs requires isolating the performance of primary workloads from co-located noisy neighbors. Lack of performance isolation arises due to contention over shared memory resources, such as last-level cache space, main memory bandwidth, and DRAM banks. Hardware vendors like Intel have started introducing techniques like Resource Director Technology (RDT) to manage shared memory resources. In the context of RDT, a promising feature is Memory Bandwidth Allocation (MBA).

In this presentation, we study whether MBA is capable of providing strong isolation via main memory bandwidth management. In doing so, we make a series of surprising discoveries. Come to the presentation to have a look at these and see joint management of main memory and LLC bandwidth by combining MBA and budget-based regulation.


Parul Sohal

Parul Sohal is a PhD student at Boston University. She is currently exploring her research interests in multi-cloud platforms.

Thursday September 24, 2020 13:40 - 14:00 EDT
Belly Virtual

Attendees (7)