FatTreeSim: Modeling a Large-scale Fat-Tree Network for HPC Systems and Data Centers Using Parallel and Discrete Event Simulation

Abstract

Fat-tree topologies have been widely adopted as the commu- nication network in data centers in the past decade. Nowa- days, high-performance computing (HPC) system designers are considering using fat-tree as the interconnection network for the next generation supercomputers. For extreme-scale computing systems like the data centers and supercomput- ers, the performance is highly dependent on the intercon- nection networks. In this paper, we present Fat TreeSim, a PDES-based toolkit consisting of a highly scalable fat-tree network model, with the goal of better understanding the de- sign constraints of fat-tree networking architectures in data centers and HPC systems, as well as evaluating the applica- tions running on top of the network. Fat TreeSim is designed to model and simulate large-scale fat-tree networks up to millions of nodes with protocol-level fidelity. We have con- ducted extensive experiments to validate and demonstrate the accuracy, scalability and usability of Fat TreeSim. On Argonne Leadership Computing Facility's Blue Gene/Qsys- tem, Mira, Fat TreeSim is capable of achieving a peak event rate of 305 M/s for a 524,288-node fat-tree model with a total of 567 billion committed events. The strong scaling experiments use up to 32,768 cores and show a near linear scalability. Comparing with a small-scale physical system in Emulab, Fat TreeSim can accurately model the latency in the same fat-tree network with less than 10% error rate for most cases. Finally, we demonstrate Fat TreeSim's usability through a case study in which Fat TreeSim serves as the net- work module of the YARNsim system, and the error rates for all test cases are less than 13.7%.

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