Scalable hardware support for conditional parallelization

Abstract

Parallel programming approaches based on task division/-spawning are getting increasingly popular because they provide for a simple and elegant abstraction of parallelization, while achieving good performance on workloads which are traditionally complex to parallelize due to the complex control flow and data structures involved. The ability to quickly distribute fine-granularity tasks among many cores is key to the efficiency and scalability of such division-based parallel programming approaches. For this reason, several hardware supports for work stealing environments have already been proposed. However, they all rely on a central hardware structure for distributing tasks among cores, which hampers the scalability and efficiency of these schemes. In this paper, we focus on conditional division, a division-based parallel approach which provides the additional benefit, over work-stealing approaches, of releasing the user from dealing with task granularity and which does not clog hardware resources with an exceedingly large number of small tasks. For this type of division-based approaches, we show that it is possible to design hardware support for speeding up task division that entirely relies on local information, and which thus exhibits good scalability properties.