Parallel Sections: Scaling System-Level Data-Structures

As systems continue to increase the number of cores within cache coherency domains, traditional techniques for enabling parallel computation on data-structures are increasingly strained. A single contended cache-line bouncing between different caches can prohibit continued performance gains with additional cores. New abstractions and mechanisms are required to reassess how data-structure consistency can be provided, while maintaining stable per-core access latencies.This paper presents the Parallel Sections (PARSEC) abstraction for mediating access to shared data-structures. Fundamental to the approach is a new form of scalable memory reclamation that leverages fast local access to real-time to globally order system events. This approach attempts to minimize coherency-traffic, while harnessing the benefit of shared read-mostly cache-lines. We show that the co-management of scalable memory reclamation, memory allocation, locking, and namespace management enables scalable system service implementation. We apply PARSEC to both memcached, and virtual memory management in a microkernel, and find order-of magnitude performance increases on a four socket, 40 core machine, and 30x lower 99th percentile latencies for virtual memory management.