Introducing enhanced fully-adaptive routing decisions within Torus-Mesh and hypercube interconnect networks

Abstract

The method for communicating within an interconnection network, or fabric of connections between nodes, can be as diverse as are the applications which utilize them. Because of dynamic traffic loads on these interconnection networks, fully-adaptive routing algorithms have been shown to exploit locality while balancing loads and softening the effects of hot-spots. One issue which has been overlooked is the impact of data traveling along the periphery of a selected minimal routable quadrant (MRQ) within these fully-adaptive algorithms. As data aligns with the destination in the x, y, and z dimensions for instance, the data then traverses the periphery of an MRQ. For each dimension that this occurs, the data is given one less choice for routing around hotspots which could appear later along the path. By weighting the decision of selecting a next-hop by avoiding the periphery of the selected MRQ, the data then has more options for avoiding hotspots. One hybridized routing algorithm which borrows heavily from CQR (an efficient and stable fully-adaptive algorithm), is introduced within this work. Enhanced CQR with Periphery Avoidance, attempts to weight the routing decision for a next hop using both output queues and the proximity to the periphery of the MRQ. This fully-adaptive algorithm is tested using simulations and a laboratory research cluster using a USB interconnect in the hypercube topology. It is also compared against other static, oblivious, and adaptive algorithms. Thor's Tack Hammer, the Kansas State University research cluster, is also benchmarked and discussed as an inexpensive and dependable parallel system.