Abstract-Network-on-Chips (NoCs) are becoming integral parts of modern microprocessors as the number of cores and modules integrated on a single chip continues to increase. Research and development of future NoC technology relies on accurate modeling and simulations to evaluate the performance impact and analyze the cost of novel NoC architectures. In this work, we present BookSim, a cycle-accurate simulator for NoCs. The simulator is designed for simulation flexibility and accurate modeling of network components. It features a modular design and offers a large set of configurable network parameters in terms of topology, routing algorithm, flow control, and router microarchitecture, including buffer management and allocation schemes. BookSim furthermore emphasizes detailed implementations of network components that accurately model the behavior of actual hardware. We have validated the accuracy of the simulator against RTL implementations of NoC routers.

Congestion caused by hot-spot traffic can significantly degrade the performance of a computer network. In this study, we present the Speculative Reservation Protocol (SRP), a new network congestion control mechanism that relieves the effect of hot-spot traffic in high bandwidth, low latency, lossless computer networks. Compared to existing congestion control approaches like Explicit Congestion Notification (ECN), which react to network congestion through packet marking and rate throttling, SRP takes a proactive approach of congestion avoidance. Using a light-weight endpoint reservation scheme and speculative packet transmission, SRP avoids hot-spot congestion while incurring minimal overhead. Our simulation results show that SRP responds more rapidly to the onset of severe hot-spots than ECN and has a higher network throughput on bursty network traffic. SRP also performs comparably to networks without congestion control on benign traffic patterns by reducing the latency and throughput overhead commonly associated with reservation protocols.

Abstract-This paper introduces Adaptive Backpressure, a novel scheme that improves the utilization of dynamically managed router input buffers by continuously adjusting the stiffness of the flow control feedback loop in response to observed traffic conditions. Through a simple extension to the router's flow control mechanism, the proposed scheme heuristically limits the number of credits available to individual virtual channels based on estimated downstream congestion, aiming to minimize the amount of buffer space that is occupied unproductively. This leads to more efficient distribution of buffer space and improves isolation between multiple concurrently executing workloads with differing performance characteristics.Experimental results for a 64-node mesh network show that Adaptive Backpressure improves network stability, leading to an average 2.6× increase in throughput under heavy load across traffic patterns. In the presence of background traffic, the proposed scheme reduces zero-load latency by an average of 31 %. Finally, it mitigates the performance degradation encountered when latency-and throughput-optimized execution cores contend for network resources in a heterogeneous chip multi-processor; across a set of PARSEC benchmarks, we observe an average reduction in execution time of 34 %.