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Exploiting Concurrency and Heterogeneity for Energy-efficient Computing : An Actor-based Approach
To accommodate energy efficiency, cloud providers started looking into radical ways of reducing the energy consumption. Energy-efficient optimizations should be addressed at both software and hardware levels of the datacenter. There have been numerous improvements in reducing the energy consumption on the hardware level. While they are efficient, however, their usage alone does not warrant significant decrease of energy dissipation. I argue that software-based methods for energy efficiency have not received as much attention as hardwarebased methods. As such, in this thesis, an important target is to provide a software framework that adapts itself in many different aspects in order to satisfy application performance and energy consumption requirements. For developing such a framework, I primarily concentrate on message passing models and, in particular, on the actor model. The actor model is arguably too conservative in its default concurrent settings. Specifically, I have identified a number of issues with the default concurrency settings of the actor model, which are: (1) message queuing delay during coordinated actions, (2) sequential message processing, (3) performance problems for concurrent message processing during high contention, and (4) the inability of the actor model to seamlessly exploit GPU resources. I use transactional memory for optimizing actor model’s message passing process, as well as propose DSL support for introducing GPU support. By addressing the identified problems I show that we can significantly improve performance, energy efficiency and programmability in the actor model.