Scalable and realistic benchmark synthesis for efficient NoC performance evaluation: A complex network analysis approach

The complexity of the design-space exploration of large-scale NoCs is exacerbated not only by the ever-increasing number of cores, but also by the increased runtime uncertainties in both the scale and task structure of the emerging applications. Consequently, it is crucial to develop rigorous mathematical frameworks for capturing the task dependencies of varied applications to foster the generation of realistic benchmarks that can guide the NoC design. However, the current NoC benchmark suites either lack portability and poorly scale as they require intensive development efforts on specific architectures and simulation time, or are synthesized based on purely stochastic models that are disconnected with real applications, which may easily lead to biased and/or delayed design choices. To overcome these drawbacks, we propose a benchmark synthesis framework that i) not only allows extraction of dynamical task dependencies of the application and synthesize traffic workloads spatio-temporally consistent with realistic traffic behavior, ii) but can also be easily scaled by the proposed complex-network inspired algorithm for large benchmark generation while preserving key structural features that governs application communication behaviors. We validate the proposed framework on a large-scale simulation environment by running a set of real applications. Experimental results show that the synthesized benchmarks respect the traffic patterns of the original applications and preserve key features of application task structures.