Networks-on-Chip

Networks-on-chip (NoCs) have been proposed as a viable solution to solving the communication problem in multicore systems. In this new setup, mapping multiple applications on available computational resources leads to interaction and contention at various network resources. Consequently, taking into account the traffic characteristics becomes of crucial importance for performance analysis and optimization of the communication infrastructure, as well as proper resource management. Although queuing-based approaches have been traditionally used for performance analysis purposes, they cannot properly account for many of the traffic characteristics (e.g., non-stationarity, self-similarity) that are crucial for multicore platform design. To overcome these limitations, we propose a statistical physics inspired approach to capture the traffic dynamics in multicore systems. As shown later in this paper, this is of fundamental significance for re-thinking the very basis of multicore systems design; it also opens up new research directions into NoC optimization which require accurate models of time-dependent and space-dependent traffic behavior.

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 re-thinking 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.

Multi-core SoCs have been widely studied as the need for high speed computing increases. Networks-on-chip (NoCs) have been proposed as a viable solution to solving the communication problem in multicore systems. In this paradigm, mapping applications on various available resources leads to an extensive interaction and communication network. Therefore, a rigorous traffic analysis of the network becomes important so as to come up with an optimal network topology and mapping algorithms, apart from efficient resource management. Many of the traffic characteristics like self-similarity (fractal nature) and non-stationarity are not captured by the traditional Poisson and Queuing based approaches, and hence a new model that encompasses time and space dependent traffic behavior.

This paper identifies non-stationary effects in grid like Network-on-Chip (NoC) traffic and proposes QuaLe, a novel statistical physics-inspired model, that can account for non-stationarity observed in packet arrival processes. Using a wide set of real application traces, we demonstrate the need for a multi-fractal approach and analyze various packet arrival properties accordingly. As a case study, we show the benefits of our multifractal approach in estimating the probability of missing deadlines in packet scheduling for chip multiprocessors (CMPs).