2017/09/30 by Osama Talaat Ibrahim, Ibrahim, Osama Talaat, Ahmed El-Mahdy +1
Computer Science · #Distributed #FOS: Computer and information sciences #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC) #cs.DC #cs.PF
paper · pdf · doi:10.48550/arxiv.1710.00122
IEEE International Conferences on Scalable Computing and Communications (ScalCom) 2016
arxiv created 2017/09/30 · arxiv updated 2017/10/03
Nowadays, multiprocessing is mainstream with exponentially increasing number of processors. Load balancing is, therefore, a critical operation for the efficient execution of parallel algorithms. In this paper we consider the fundamental class of tree-based algorithms that are notoriously irregular, and hard to load-balance with existing static techniques. We propose a hybrid load balancing method using the utility of statistical random sampling in estimating the tree depth and node count distributions to uniformly partition an input tree. To conduct an initial performance study, we implemented the method on an Intel Xeon Phi accelerator system. We considered the tree traversal operation on both regular and irregular unbalanced trees manifested by Fibonacci and unbalanced (biased) randomly generated trees, respectively. The results show scalable performance for up to the 60 physical processors of the accelerator, as well as an extrapolated 128 processors case.