Affinité entre les processus, métriques et impact sur les performances : étude expérimentale
| Autor: | Emmanuel Jeannot, Cyril Bordage |
|---|---|
| Přispěvatelé: | Topology-Aware System-Scale Data Management for High-Performance Computing (TADAAM), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Jeannot, Emmanuel, Inria Bordeaux Sud-Ouest, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
020203 distributed computing
Measure (data warehouse) algorithm performance metric Computer science communication Process (computing) 020206 networking & telecommunications Topology (electrical circuits) 02 engineering and technology Network topology computer.software_genre process placement Set (abstract data type) Empirical research Processor affinity 0202 electrical engineering electronic engineering information engineering [INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] MPI Data mining [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] communication modeling computer Performance metric topology mapping |
| Zdroj: | 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (IEEE/ACM CCGrid) 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (IEEE/ACM CCGrid), May 2018, Washington DC, United States [Research Report] RR-9132, Inria Bordeaux Sud-Ouest. 2017 CCGrid |
| Popis: | International audience; Process placement, also called topology mapping, is a well-known strategy to improve parallel program execution by reducing the communication cost between processes. It requires two inputs: the topology of the target machine and a measure of the affinity between processes. In the literature, the dominant affinity measure is the communication matrix that describes the amount of communication between processes. The goal of this paper is to study the accuracy of the communication matrix as a measure of affinity. We have done an extensive set of tests with two fat-tree machines and a 3d-torus machine to evaluate several hypotheses that are often made in the literature and to discuss their validity. First, we check the correlation between algorithmic metrics and the performance of the application. Then, we check whether a good generic process placement algorithm never degrades performance. And finally, we see whether the structure of the communication matrix can be used to predict gain. I. INTRODUCTION We are currently seeing a deepening in the hierarchy of high-performance computing system. Nodes are composed of multicore processors with different levels of memory (standard DRAM, non-volatile memory, faster but smaller MCDRAM for KNL, etc.) and the network interconnecting these nodes can also be highly intricate with complex topology and high diameter. The consequence of these architectural features is that the performance of the parallel applications highly depends on the nodes allocated for the job as well as the mapping of these jobs. Process placement (also known as topology mapping) is an active field of research that deals with the development of strategies targeting the improvement of parallel applications by carefully allocating processes onto the resources [14]. The goal is to reduce the communication by mapping close to each other processes that communicate the most. The communication time depends on the algorithm implemented in the application: it depends on the quantity of data to be exchanged. Moreover, since all computing resources are not directly connected, it also depends on the distance between the running processes as well as the speed of the different links. Figure 1 shows what can be the distances (in number of hops) between cores in a fat-tree machine with 6 nodes with 24 cores each (two processors made of two NUMA nodes with 6 cores each). We see clearly blocks of same distances. Hence, it seems natural to put closer two processes that communicate a lot to reduce the communication cost. To this purpose, we need to adapt the execution of parallel applications to the target machine according to its specific topology. |
| Databáze: | OpenAIRE |
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