The impact of memory subsystem resource sharing on datacenter applications

In this paper we study the impact of sharing memory resources on five Google datacenter applications: a web search engine, bigtable, content analyzer, image stitching, and protocol buffer. While prior work has found neither positive nor negative effects from cache sharing across the PARSEC benchmark suite, we find that across these datacenter applications, there is both a sizable benefit and a potential degradation from improperly sharing resources. In this paper, we first present a study of the importance of thread-tocore mappings for applications in the datacenter as threads can be mapped to share or to not share caches and bus bandwidth. Second, we investigate the impact of co-locating threads from multiple applications with diverse memory behavior and discover that the best mapping for a given application changes depending on its co-runner. Third, we investigate the application characteristics that impact performance in the various thread-to-core mapping scenarios. Finally, ...

• Type: Journal
• Year: 2011
• Where: ISCA
• Authors: Lingjia Tang, Jason Mars, Neil Vachharajani, Robert Hundt, Mary Lou Soffa
• Keywords: Bus Bandwidth, Computer Systems Organization, Core Decisions, Hardware, ISCA 2011