Global adaptive routing exploits non-minimal paths to improve performance on adversarial traffic patterns and load-balance network channels in large-scale networks. However, most prior work on global adaptive routing have assumed admissible traffic pattern where no endpoint node is oversubscribed. In the presence of a greedy flow or hotspot traffic, we show how exploiting path diversity with global adaptive routing can spread network congestion and degrade performance. When global adaptive routing is combined with congestion management, the two types of congestion - network congestion that occurs within the interconnection network channels and endpoint congestion that occurs from oversubscribed endpoint nodes - are not properly differentiated. As a result, previously proposed congestion management mechanisms that are effective in addressing endpoint congestion are not necessarily effective when global adaptive routing is also used in the network. Thus, we propose a novel, low-cost contention-based congestion management (CBCM) to identify endpoint congestion based on the contention within the intermediate routers and at the endpoint nodes. While contention also occurs for network congestion, the endpoint nodes or the destination determines whether the congestion is endpoint congestion or network congestion. If it is only network congestion, CBCM ignores the network congestion and adaptive routing is allowed to minimize network congestion. However, if endpoint congestion occurs, CBCM throttles the hotspot senders and minimally route the traffic through a separate VC. Our evaluation across different traffic patterns and network sizes demonstrates that our approach is more robust in identifying endpoint congestion in the network while complementing global adaptive routing to avoid network congestion.

Accurately estimating congestion for proper global adaptive routing decisions (i.e., determine whether a packet should be routed minimally or non-minimally) has a significant impact on overall performance for high-radix topologies, such as the Dragonfly topology. Prior work have focused on understanding near-end congestion - i.e., congestion that occurs at the current router - or downstream congestion - i.e., congestion that occurs in downstream routers. However, most prior work do not evaluate the impact of far-end congestion or the congestion from the high channel latency between the routers. In this work, we refer to far-end congestion as phantom congestion as the congestion is not "real" congestion. Because of the long inter-router latency, the in-flight packets (and credits) result in inaccurate congestion information and can lead to inaccurate adaptive routing decisions. In addition, we show how transient congestion occurs as the occupancy of network queues fluctuate due to random traffic variation, even in steady-state conditions. This also results in inaccurate adaptive routing decisions that degrade network performance with lower throughput and higher latency. To overcome these limitations, we propose a history-window based approach to remove the impact of phantom congestion. We also show how using the average of local queue occupancies and adding an offset significantly remove the impact of transient congestion. Our evaluations of the adaptive routing in a large-scale Dragonfly network show that the combination of these techniques results in an adaptive routing that nearly matches the performance of an ideal adaptive routing algorithm.

The Robot Control C Library (RCCL) is a system for developing robot control programs in a UNIX environment, and has proven to be useful in research applications. Work undertaken in expanding RCCL to handle multiple robots, and upgrading its implementation to a multiple-processor microVAX/UNIX system is reported. This includes (1) reworking the primitive set to allow for the specification of multiple robot actions; (2) modifying the trajectory generation mechanism so that several robots may be controlled and coordinated at once; and (3) redesigning the system interface on top of which RCCL is built to allow the creation of multiple real-time robot control tasks interfaced to UNIX.>