Load-Balanced Bottleneck Objectives in Process Mapping

We propose a new problem formulation for graph partitioning that is tailored to the needs of time-critical simulations on modern heterogeneous supercomputers

Netloc: a Tool for Topology-Aware Process Mapping

International audienceInterconnection networks in parallel platforms can be made of thousands of nodes and hundreds of switches. The communication cost between tasks of a parallel application varies significantly with their actual location in such platforms. Topology-aware process mapping consists in matching the application communication pattern with the network topology to improve the communication cost by placing related tasks close on the hardware. We show that our Netloc tool for gathering network topology in a generic way can be combined with the state-of-the-art Scotch partitioner for computing topology-aware MPI process placement. Our experiments with a stencil application on a fat-tree machine show that we are able to significantly improve the runtime in the vast majority of cases

Fast and high quality topology-aware task mapping

Considering the large number of processors and the size of the interconnection networks on exascale capable supercomputers, mapping concurrently executable and communicating tasks of an application is a complex problem that needs to be dealt with care. For parallel applications, the communication overhead can be a significant bottleneck on scalability. Topology-aware task-mapping methods that map the tasks to the processors (i.e., cores) by exploiting the underlying network information are very effective to avoid, or at worst bend, this limitation. We propose novel, efficient, and effective task mapping algorithms employing a graph model. The experiments show that the methods are faster than the existing approaches proposed for the same task, and on 4096 processors, the algorithms improve the communication hops and link contentions by 16% and 32%, respectively, on the average. In addition, they improve the average execution time of a parallel SpMV kernel and a communication-only application by 9% and 14%, respectively