Adaptive Backpressure: Efficient buffer management for on-chip networks

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%.