Browsing by Subject "Hypergraph"
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Item Open Access Investigation of load balancing scalability in space plasma simulations(Springer, Berlin, Heidelberg, 2013) Türk, Ata; Demirci, Gündüz V.; Aykanat, Cevdet; Von Alfthan, S.; Honkonen I.In this study we report the load-balancing performance issues that are observed during the petascaling of a space plasma simulation code developed at the Finnish Meteorological Institute (FMI). The code models the communication pattern as a hypergraph, and partitions the computational grid using the parallel hypergraph partitioning scheme (PHG) of the Zoltan partitioning framework. The result of partitioning determines the distribution of grid cells to processors. It is observed that the initial partitioning and data distribution phases take a substantial percentage of the overall computation time. Alternative (graph-partitioning-based) schemes that provide better balance are investigated. Comparisons in terms of effect on running time and load-balancing quality are presented. Test results on Juelich BlueGene/P cluster are reported. © 2013 Springer-Verlag.Item Open Access ON two-dimensional sparse matrix partitioning: models, methods, and a recipe(Society for Industrial and Applied Mathematics, 2010) Çatalyürek, U. V.; Aykanat, Cevdet; Uçar, A.We consider two-dimensional partitioning of general sparse matrices for parallel sparse matrix-vector multiply operation. We present three hypergraph-partitioning-based methods, each having unique advantages. The first one treats the nonzeros of the matrix individually and hence produces fine-grain partitions. The other two produce coarser partitions, where one of them imposes a limit on the number of messages sent and received by a single processor, and the other trades that limit for a lower communication volume. We also present a thorough experimental evaluation of the proposed two-dimensional partitioning methods together with the hypergraph-based one-dimensional partitioning methods, using an extensive set of public domain matrices. Furthermore, for the users of these partitioning methods, we present a partitioning recipe that chooses one of the partitioning methods according to some matrix characteristics. © 2010 Society for Industrial and Applied Mathematics.Item Open Access Optimizing nonzero-based sparse matrix partitioning models via reducing latency(Academic Press, 2018) Acer, S.; Selvitopi, O.; Aykanat, CevdetFor the parallelization of sparse matrix-vector multiplication (SpMV) on distributed memory systems, nonzero-based fine-grain and medium-grain partitioning models attain the lowest communication volume and computational imbalance among all partitioning models. This usually comes, however, at the expense of high message count, i.e., high latency overhead. This work addresses this shortcoming by proposing new fine-grain and medium-grain models that are able to minimize communication volume and message count in a single partitioning phase. The new models utilize message nets in order to encapsulate the minimization of total message count. We further fine-tune these models by proposing delayed addition and thresholding for message nets in order to establish a trade-off between the conflicting objectives of minimizing communication volume and message count. The experiments on an extensive dataset of nearly one thousand matrices show that the proposed models improve the total message count of the original nonzero-based models by up to 27% on the average, which is reflected on the parallel runtime of SpMV as an average reduction of 15% on 512 processors.Item Open Access A Recursive Hypergraph Bipartitioning Framework for Reducing Bandwidth and Latency Costs Simultaneously(IEEE Computer Society, 2017) Selvitopi, O.; Acer, S.; Aykanat, CevdetIntelligent partitioning models are commonly used for efficient parallelization of irregular applications on distributed systems. These models usually aim to minimize a single communication cost metric, which is either related to communication volume or message count. However, both volume- and message-related metrics should be taken into account during partitioning for a more efficient parallelization. There are only a few works that consider both of them and they usually address each in separate phases of a two-phase approach. In this work, we propose a recursive hypergraph bipartitioning framework that reduces the total volume and total message count in a single phase. In this framework, the standard hypergraph models, nets of which already capture the bandwidth cost, are augmented with message nets. The message nets encode the message count so that minimizing conventional cutsize captures the minimization of bandwidth and latency costs together. Our model provides a more accurate representation of the overall communication cost by incorporating both the bandwidth and the latency components into the partitioning objective. The use of the widely-adopted successful recursive bipartitioning framework provides the flexibility of using any existing hypergraph partitioner. The experiments on instances from different domains show that our model on the average achieves up to 52 percent reduction in total message count and hence results in 29 percent reduction in parallel running time compared to the model that considers only the total volume. © 2016 IEEE.