Leveraging on Deep Memory Hierarchies to Minimize Energy Consumption and Data Access Latency on Single-Chip Cloud Computers

Abstract

Recent advances in chip design and integration technologies have led to the development of Single-Chip Cloud computers which are a microcosm of cloud datacenters. Those computers are based on Network-on-Chip (NoC) architectures with deep memory hierarchies. Developing scheduling algorithms to reduce data access latency as well as energy consumption is a major challenge for such architectures. In this paper, we propose a set of algorithms to jointly address the problem of task scheduling and data allocation in a unified approach. Moreover, we present a feasible system model for NoC based multicores considering a three-level memory hierarchy that effectively captures the energy consumed by various elements of system including: processing cores, caches, and NoC subsystem. Simulation results show the superiority of proposed algorithms compared to two state-of-the-art algorithms found in the literature. The experimental results clearly indicate that algorithms performing data and task scheduling in a joint fashion are superior against techniques implementing task and data scheduling separately.