Mapping Parallel Components into Processing Elements Sample Clauses

Mapping Parallel Components into Processing Elements. Improving the layout of the tasks of a parallel application on a particular hardware architecture is an attractive research topic as it may increase the performance of the application without requiring modifications to the source code. The advantages may be particularly significant if the supporting (a) 3-D mesh of the blood vessel 3 7 1 0 5 4 6 2 3 7 1 0 4 (b) Relative communication volume (c) Relative message quantity Figure 4.1: The simulated blood vessel and its exemplary partitioning for parallel processing computing machines are high-performance clusters with non-uniform interconnection network links, and the optimized placement is able to map to the capabilities of cutting edge network solutions. Several projects attempt to address some or all aspects of this issue. Rubik [130] is a software toolkit that applies simple geometry transformations (e.g., splits, tilts) to the Cartesian task topology of an application, altering its mapping to the Cartesian network topology. Thanks to task-shuffling, the underlying hardware may better support MPI collective operations by utilizing more hardware links while avoiding excessive latency or congestion [53]. Our solution follows a similar approach: we design the layout of MPI tasks before we execute the application. However, as our hemodynamic CFD code mainly uses point-to-point communication and has no statically defined communication topology, an analysis of data exchange profiles is needed for each execution and each input of the application in order to determine communication patterns and optimize task mapping. The process placement mechanism is greatly facilitated by the current versions of MPI frameworks. OpenMPI [131] provides several mechanisms supporting process arrangements, starting from simple, built-in process iterations over the set of available hosts to the process binding maps. Moreover, the modular architecture of OpenMPI allows extending this functionality. Implementation of LAMA (Locality-Aware Mapping Algorithm) that is described in [54] follows the OpenMPI Resource Mapping Subsystem interface extending the execution capabilities of OpenMPI. By applying this component, users can control cache- and NUMA-aware process placement specifying a process layout which assigns the sequence of MPI processes with nodes, boards, CPU sockets, cores, and hardware-supported threads. To gain the locality-aware performance benefits, this description should reflect the application communication requirements. H...
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