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Simulating Application Agnostic Process Assignment for Graph Workloads on Dragonfly and Fat Tree Topologies
SessionResearch Posters Display
DescriptionDistributed-memory graph applications are dominated by communication and synchronization overheads. For such applications, the communication pattern comprises of variable-sized data exchanges between process neighbors in a process graph topology, which unlike process grid for rectangular problems is difficult to optimize for enhancing the locality in a sustainable fashion.
Process assignment or remapping can improve the communication performance, however, existing solutions mostly caters to Cartesian process topologies and not the graph topology. In this work, we propose application and topology agnostic process remapping strategies for graph applications. For two communication intensive distributed-memory graph applications (graph clustering and triangle counting), we demonstrate about 30% improvements in the overall execution times through various remapping methodologies via SST-based packet-level simulations on Dragonfly and Fat Tree based network topologies.
Process assignment or remapping can improve the communication performance, however, existing solutions mostly caters to Cartesian process topologies and not the graph topology. In this work, we propose application and topology agnostic process remapping strategies for graph applications. For two communication intensive distributed-memory graph applications (graph clustering and triangle counting), we demonstrate about 30% improvements in the overall execution times through various remapping methodologies via SST-based packet-level simulations on Dragonfly and Fat Tree based network topologies.
Event Type
Posters
Research Posters
TimeTuesday, 14 November 202310am - 5pm MST
LocationDEF Concourse
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