A Methodology for Handling Data Movements by Anticipation: Position Paper

  • Raphaël BleuseEmail author
  • Giorgio LucarelliEmail author
  • Denis Trystram
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


The enhanced capabilities of large scale parallel and distributed platforms produce a continuously increasing amount of data which have to be stored, exchanged and used by various tasks allocated on different nodes of the system. The management of such a huge communication demand is crucial for reaching the best possible performance of the system. Meanwhile, we have to deal with more interferences as the trend is to use a single all-purpose interconnection network whatever the interconnect (tree-based hierarchies or topology-based heterarchies). There are two different types of communications, namely, the flows induced by data exchanges during the computations, and the flows related to Input/Output operations. We propose in this paper a general model for interference-aware scheduling, where explicit communications are replaced by external topological constraints. Specifically, the interferences of both communication types are reduced by adding geometric constraints on the allocation of tasks into machines. The proposed constraints reduce implicitly the data movements by restricting the set of possible allocations for each task. This methodology has been proved to be efficient in a recent study for a restricted interconnection network (a line/ring of processors which is an intermediate between a tree and higher dimensions grids/torus). The obtained results illustrated well the difficulty of the problem even on simple topologies, but also provided a pragmatic greedy solution, which was assessed to be efficient by simulations. We are currently extending this solution for more complex topologies. This work is a position paper which describes the methodology, it does not focus on the solving part.


Scheduling Affinity Data movements Heterogeneity Topology HPC 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIGGrenobleFrance
  2. 2.FSTC/CSCUniversity of LuxembourgLuxembourg CityLuxembourg

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