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he Universal Globally Adaptive Load-balance Routing (UGAL) with global information, referred as UGAL-G, represents an ideal form of adaptive routing on Dragonfly. UGAL-G is impractical to implement, however, since the global information cannot be maintained accurately. Practical adaptive routing schemes, such as UGAL with local information (UGAL-L), performs noticeably worse than UGAL-G. In this work, we investigate a machine learning approach for routing on Dragonfly. Specifically, we develop a machine learning-based routing scheme, called UGAL-ML, that is capable of making routing decisions like UGAL-G based only on the information local to each router. Our preliminary evaluation indicates that UGAL-ML can achieve comparable performance to UGAL-G for some traffic patterns. 

The Dragonfly network has been deployed in the current generation supercomputers and will be used in the next generation supercomputers. The Universal Globally Adaptive Load-balance routing (UGAL) is the state-of-the-art routing scheme for Dragonfly. In this work, we show that the performance of the conventional UGAL can be further improved on many practical Dragonfly networks, especially the ones with a small number of groups, by customizing the paths used in UGAL for each topology. We develop a scheme to compute the custom sets of paths for each topology and compare the performance of our topology-custom UGAL routing (T-UGAL) with conventional UGAL. Our evaluation with different UGAL variations and different topologies demonstrates that by customizing the routes, T-UGAL offers significant improvements over UGAL on many practical Dragonfly networks in terms of both latency when the network is under low load and throughput when the network is under high load. 

Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.