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Mapping filtering streaming applications with communication costs

Kunal Agrawal, Anne Benoit, Fanny Dufossé, Yves Robert

2009
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Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures - SPAA '09
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In this paper, we explore the problem of mapping filtering streaming applications on large-scale homogeneous platforms, with a particular emphasis on communication models and their impact. Filtering application are streaming applications where each node also has a selectivity which either increases or decreases the size of its input data set. This selectivity makes the problem of scheduling these applications more challenging than the more studied problem of scheduling "non-filtering" streaming
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... workflows. We identify three significant realistic communication models. For each of them, we address the complexity of the following important problems: 1. Given an execution graph, how can one compute the period and latency? A solution to this problem is an operation list which provides the time-steps at which each computation and each communication occurs in the system. 2. Given a filtering workflow problem, how can one compute the schedule that minimizes the period or latency? A solution to this problem requires generating both the execution graph and the associated operation list. Altogether, with three models, two problems and two objectives, we present 12 complexity results, thereby providing solid theoretical foundations for the study of filtering streaming applications. 0 As in previous work [1, 2], we consider one-to-one mappings, where each server has at most one service mapped to it. Clearly, for one-to-one mappings, the number of servers must be equal to or more than the number of services. We show in this paper that most period or latency minimization are NP-complete even in this setting 1 . Note that we do not need to specify which service is mapped onto which server, since all servers are equivalent. Instead, we have to generate the execution graph together with the operation list, in order to minimize the period or latency. The emphasis of our work is on the impact of communication models. We consider two commonly used communication models. The no overlap communication model requires that at any point, a server can either compute, or receive an incoming communication, or send an outgoing communication. This models single threaded machines where every operation is serialized. We define two variants for this model, one where we enforce in-order execution, and another where we allow out-of-order execution (which means interleaving communications and computations of different data sets) so as to reduce the idle-time incurred by the serial ordering of the communications. In contrast, the overlap communication model considers the situation where a server can compute and send/receive communications at the same time. This calls for multithreaded machines and parallel communications. In all models, both computations and communications are non-preemptive, which means that they cannot be interrupted once initiated. Also, communications are synchronous (by rendez-vous between the sender and the receiver). This synchronization between servers can cause idle times. Our main findings is that computing the period or the latency in all these models turns out to be difficult. As already stated, the minimization problems (finding the optimal plan to minimize the period or the latency) are all NP-hard. This result is surprising, since polynomial algorithms exist for homogeneous machines when we do not model communication [1, 2] . Therefore, modeling communication costs explicitly has a huge impact on the difficulty of mapping filtering services. In addition, and quite unexpectedly, the "orchestration" problems (given an execution graph, find the optimal operation list) also are of combinatorial nature. Finally, the choice of the model has a tremendous impact on the values that can be achieved. Many of our results and counter-examples apply to regular workflows (without selectivities), and should be of great interest to the whole community interested in scheduling streaming applications. This paper is organized as follows. Section 2 describes the framework of the problem in more details. Section 3 illustrates the difference between communication models with the help of several examples. The next two sections constitute the core of the paper. Section 4 is devoted the period minimization problem, while Section 5 is the counterpart for latency minimization. Finally we give some conclusions and perspectives in Section 6.

doi:10.1145/1583991.1583997
dblp:conf/spaa/AgrawalBDR09
fatcat:d5fjjurk2jhfradve5ps2zbcpe