DistDLB

DistDLB: improving cosmology SAMR simulations on distributed computing systems through hierarchical load balancing Cosmology SAMR simulations have played a prominent role in the field of astrophysics. The emerging distributed computing systems provide an economic alternative to the traditional parallel machines, and enable scientists to conduct cosmological simulations that require vast computing power. An important issue of conducting distributed cosmological simulations is about performance and efficiency. In this paper, we present a dynamic load balancing scheme called DistDLB that is designed to improve the performance of distributed cosmology simulations. Distributed systems, e.g. the Computation Grid, usually consist of heterogeneous resources connected with shared networks. By considering these features of distributed systems and unique characteristics of cosmology SAMR simulations, DistDLB focuses on reducing the redistribution cost through a hierarchical load balancing approach and a run-time decision making mechanism. Heuristic methods have been proposed to adaptively adjust load balancing strategies based on the observation of the current system and application state. Our experiments with real-world cosmology simulations on production systems indicate that the proposed DistDLB scheme can effectively improve the performance of cosmology simulations by 2.56 – 79.14% as compared to the scheme that does not consider the heterogeneous and dynamic features of distributed systems.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Schornbaum, Florian; Rüde, Ulrich: Extreme-scale block-structured adaptive mesh refinement (2018)
  2. Ibanez, Daniel A.; Seol, E. Seegyoung; Smith, Cameron W.; Shephard, Mark S.: PUMI: parallel unstructured mesh infrastructure (2016)
  3. Wiens, Jeffrey K.; Stockie, John M.: An efficient parallel immersed boundary algorithm using a pseudo-compressible fluid solver (2015)
  4. Bhalla, Amneet Pal Singh; Bale, Rahul; Griffith, Boyce E.; Patankar, Neelesh A.: A unified mathematical framework and an adaptive numerical method for fluid-structure interaction with rigid, deforming, and elastic bodies (2013)
  5. Kuan, Chih-Kuang; Sim, Jaeheon; Shyy, Wei: Adaptive thermo-fluid moving boundary computations for interfacial dynamics (2012)
  6. Kanarska, Y.; Lomov, I.; Antoun, T.: Mesoscale simulations of particulate flows with parallel distributed Lagrange multiplier technique (2011)
  7. Park, Kyoungsoo; Paulino, Glaucio H.: Parallel computing of wave propagation in three-dimensional functionally graded media (2011)
  8. Thornburg, Jonathan: Adaptive mesh refinement for characteristic grids (2011)
  9. Walshaw, Chris: Multilevel refinement for combinatorial optimisation: boosting metaheuristic performance (2008) ioport
  10. Lan, Zhiling; Taylor, Valerie E.; Li, Yawei: DistDLB: improving cosmology SAMR simulations on distributed computing systems through hierarchical load balancing (2006)