An External-Memory Work-Depth Model and Its Applications to Massively Parallel Join Algorithms Xiao Hu Ke Yi Paraschos Koutris Hong Kong University of Science and Technology University of Wisconsin-Madison (xhuam,yike)@cse.ust.hk
[email protected] ABSTRACT available. This allows us to better focus on the inherent parallel The PRAM is a fundamental model in parallel computing, but it is complexity of the algorithm. On the other hand, given a particular p, seldom directly used in parallel algorithm design. Instead, work- an algorithm with work W and depth d can be optimally simulated depth models have enjoyed much more popularity, as they relieve on the PRAM in O¹d + W /pº steps [11–13]. Thirdly, work-depth algorithm designers and programmers from worrying about how models are syntactically similar to many programming languages various tasks should be assigned to each of the processors. Mean- supporting parallel computing, so algorithms designed in these while, they also make it easy to study the fundamental parallel models can be more easily implemented in practice. complexity of the algorithm, namely work and depth, which are The PRAM can be considered as a fine-grained parallel model, irrelevant to the number of processors available. where in each step, each processor carries out one unit of work. The massively parallel computation (MPC) model, which is a However, today’s massively parallel systems, such as MapReduce simplified version of the BSP model, has drawn a strong interest and Spark, are better captured by a coarse-grained model. The first in recent years, due to the widespread popularity of many big data coarse-grained parallel model is the bulk synchronous parallel (BSP) systems based on such a model.