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A model for thread and memory placement on NUMA systems Funston, Justin

Abstract

The problem of placement of threads, or virtual cores, on physical cores in a multicore system has been studied for over a decade. Despite this effort, we still do not know how to assign virtual to physical cores on a non-uniform memory access (NUMA) system so as to meet a performance target while minimizing resource consumption. Prior work has made large strides in this area, but these solutions either addressed hardware with specific properties, leaving us unable to generalize the models to other systems, or modeled much simpler effects than the actual performance in different placements. An interdependent problem is how to place memory on NUMA systems. Poor memory placement causes congestion on interconnect links, contention for memory controllers, and ultimately long memory access times and poor performance. Commonly used operating system techniques for NUMA memory placement fail to achieve optimal performance in many cases. Our contribution is a general framework for reasoning about workload placement and memory placement on machines with shared resources. This framework enables us to automatically build an accurate performance model for any machine with a hierarchy of known shared resources. Using our methodology, data center operators can minimize the number of NUMA (CPU+memory) nodes allocated for an application or a service, while ensuring that it meets performance objectives. More broadly, the methodology empowers them to efficiently “pack” virtual containers on the physical hardware. We also present an effective solution for placing memory that avoids congestion on interconnects due to memory traffic and additionally selects the best page size that balances translation lookaside buffer (TLB) effects against more granular memory placement. The solutions proposed can significantly improve performance and work at the operating system level so they do not require changes to applications.

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Attribution-ShareAlike 4.0 International