Architectural techniques to unlock ordered and nested speculative parallelism

Abstract

Current multicores suffer from two major limitations: they can only exploit a fraction of the parallelism available in applications and they are very hard to program. This is because they are limited to programs with coarse-grained tasks that synchronize infrequently. However, many applications have abundant parallelism when divided into small tasks (of a few tens to hundreds of instructions each). Current systems cannot exploit this fine-grained parallelism because synchronization and task management overheads overwhelm the benefits of parallelism. This thesis presents novel techniques that tackle the scalability and programmability issues of current multicores. First, Swarm is a parallel architecture that makes fine-grained parallelism practical by leveraging order as a general synchronization primitive. Swarm programs consist of tasks with programmer-specified order constraints. Swarm hardware provides support for fine-grained task management, and executes tasks speculatively and out of order to scale. Second, Fractal extends Swarm to harness nested speculative parallelism, which is crucial to scale large, complex applications and to compose parallel speculative algorithms. Third, Amalgam makes more efficient use of speculation resources by splitting and merging address set signatures to create fixed-size units of speculative work. Amalgam can improve performance and reduce implementation costs. Together, these techniques unlock abundant fine-grained parallelism in applications from a broad set of domains, including graph analytics, databases, machine learning, and discrete-event simulation. At 256 cores, our system is 40x -512x faster than a single core system and outperforms state-of-the-art software-only parallel algorithms by one to two orders of magnitude. Besides achieving near-linear scalability, the resulting programs are almost as simple as their sequential counterparts, as they do not use explicit synchronization.