Workload characterization and its impact on multicore platform design

Networks-on-chip (NoCs) have been proposed as a scalable solution to solving the communication problem in multicore systems. Although the queuing-based approaches have been traditionally used for performance analysis purposes, they cannot properly account for many of the traffic characteristics (e.g., non-stationary, self-similarity, higher order statistics) that are crucial for multicore platform design when communication happens via the NoC approach. To overcome this limitation, we propose a mean field approach to analyze the traffic dynamics in multicore systems and show how the non-stationary effects of the NoC workload can be effectively captured; this is of fundamental significance for rethinking the very basis of multicore systems design. Moreover, our experimental results demonstrate that both network architecture and application characteristics are the main sources of power law behavior observed in network traffic. Our findings open new research directions into NoC optimization which require accurate models of time- and space-dependent traffic behavior.