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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.