Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs

Space-sharing is regarded as the proper resource management scheme for many-core OSes. For today's many-core chips and parallel programming models providing no explicit resource requirements, an important research problem is to provide a proper resource allocation to the running applications while considering not only the architectural features but also the characteristics of the parallel applications. In this paper, we introduce a space-shared scheduling strategy for shared-memory parallel programs. To properly assign the disjoint set of cores to simultaneously running parallel applications, the proposed scheme considers the performance characteristics of the executing (parallel) code section of all running applications. The information about the performance is used to compute a proper core allocation in accordance to the goal of the scheduling policy given by the system manager. We have first implemented a user-level scheduling framework that runs on Linux-based multi-core chips. A simple performance model based solely on online profile data is used to characterize the performance scalability of applications. The framework is evaluated for two scheduling policies, balancing and maximizing QoS, and on two different many-core platforms, a 64-core AMD Opteron platform and a 36-core Tile-Gx36 processor. Experimental results of various OpenMP benchmarks show that in general our space-shared scheduling outperforms the standard Linux scheduler and meets the goal of the active scheduling policy.