Exploiting Malleable Parallelism on Multicore Systems

As shared memory platforms continue to grow in core counts, the need for context-aware scheduling continues to grow. Context-aware scheduling takes into account characteristics of the system and application performance when making decisions. Traditionally applications run on a static thread count that is set at compile-time or at job start time, without taking into account the dynamic context of the system, other running applications, and potentially the performance of the application itself. However, many shared memory applications can easily be converted to malleable applications, that is, applications that can run with an arbitrary number of threads and can change thread counts during execution. Many new and intelligent scheduling decisions can be made when applications become context-aware, including expanding to ll an empty system or shrinking to accommodate a more parallelizable job. This thesis describes a prototype system called Resizing for Shared Memory (RSM), which supports OpenMP applications on shared memory platforms. RSM includes a main daemon that records performance information and makes resizing decisions as well as a communication library to allow applications to contact the RSM daemon and enact resizing decisions. Experimental results show that RSM can improve turn-around time and system utilization even using very simple heuristics and resizing policies.