Mitigating Resource Contention in Warehouse-Scale Computers
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Modern datacenters that host large-scale Internet services are extremely expensive to construct and operate. Improving software performance and server utilization is key to improving the efficiency and reducing the enormous cost in datacenters. In this talk, I present novel compilation techniques and runtime systems to significantly improve performance, quality of service (QoS) and machine utilization in datacenters by effectively mitigating memory resource contention on modern multicore servers.
Specifically, this talk presents: 1) comprehensive characterization of the impact of memory resource sharing on industry-strength large-scale datacenter workloads and the design of runtime systems to intelligently map application threads to cores to promote positive resource sharing and mitigate resource contention to improve application performance; 2) the design of novel compilation techniques and run-time systems that statically and dynamically manipulate applications' contentious nature to enable the co-location of applications with varying QoS requirements, and as a result, greatly improve server utilization in datacenters.
Lingjia Tang received her Ph.D in Computer Science from University of Virginia in May 2012. She joined UCSD CSE Department as a research faculty member in Summer 2012. Her research focuses on compiler and runtime systems, especially such systems for large scale datacenters. Recently, her publication at Microà½ is selected as IEEE Micro TopPicks. She received a best paper award at IEEE/ACM International Conference of Code Generation and Optimization (CGO) in 2012. In addition, her publication at International Symposium of Computer Architecture (ISCA) is selected as one of the excellence papers 2011 by Research at Google.