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and compilers All of these factors result in different performances across sites Second, a typical distributed system includes different types of networks where network performance gap can be of different orders of magnitude Therefore, the redistribution cost can be of more orders of magnitude than the actual partitioning when the excess workload is transferred across geographically distributed machines [13] Furthermore, the shared networking resources are highly dynamic, which makes it dif cult to make an accurate data transferring decision To ef ciently partition the application and then migrate the excess workload from overloaded processors to underloaded processors, the uctuating performance of networks should be taken into consideration Simply applying a load balancing scheme that is designed for homogeneous parallel systems to heterogeneous distributed systems can bring in a signi cant performance penalty [21].

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In this paper, we present DistDLB, a dynamic load balancing scheme for cosmology SAMR simulations running on heterogeneous distributed computing systems The primary goal of DistDLB is to reduce the redistribution cost as well as to maintain the quality of load balancing More speci cally, techniques are proposed to address the following issues: (1) the heterogeneity of networks, (2) the uctuating performance of networks, (3) the heterogeneity of processors, and (4) the unique features of cosmology simulations To address the heterogeneity of networks, DistDLB employs a hierarchical load balancing approach One of the key issues is to decide when the workload should be redistributed across remotely connected machines to produce a performance gain Heuristic methods are developed to evaluate the computational gain and the redistribution cost.

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The uctuating performance of networks is addressed by adaptively choosing an appropriate action, for example, repartitioning across sites or limiting repartitioning within each site, based on the network traf c To address the unique characteristics of cosmology applications, the load balancing technique presented in Ref [22] is adopted Experiments with the cosmology modeling code ENZO [11] on production distributed systems show that DistDLB can effectively reduce the execution times of cosmology simulations by 256 7914% depending on the underlying network performance and the application parameters, as compared to the scheme that does not consider the heterogeneous and uctuating performance of distributed systems The load balancing problem has been studied extensively over the past decades A wealth of literatures is available to cover a wide range of issues related to load balancing [5, 9, 12, 23, 29, 33, 38, 43, 47].

To date, most load balancing techniques are focused on homogeneous parallel system, where the primary objective is to maintain a good quality of load balancing as well as to reduce the interprocessor communication during the computation There are several well-known partitioning/repartitioning tools for scienti c computing; examples of such tools include JOSTLE [44, 45], ParMETIS [30, 33], Zoltan [5, 13], and GridARM [8, 50] The proposed DistDLB scheme complements existing works on load balancing in three aspects First, it targets cosmology SAMR applications that currently lack the ef cient load balancing techniques Second, DistDLB emphasizes on the techniques that reduce the substantial.

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Figure 6.11 Decomposition of a 1-D signal fm into a smooth fm+1 and a detailed signal fm+1 h: low-pass lter; g: high-pass lter.

n 1

_ Dnp = 104 n/cm2 s 1.49 1021 atoms/g 1.83 10 24 cm2/atom 0.626 MeV 1.6022 10 6 erg/MeV 3600 s/h = 9.846 10 2 erg/g h = 0.98 mrad/h

,_ _Output - - - _ _ _ _ SNR ---4

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