Issue No. 05 - May (2014 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.83
Arturo Gonzalez-Escribano , Dept. de Inf., Univ. de Valladolid, Valladolid, Spain
Yuri Torres , Dept. de Inf., Univ. de Valladolid, Valladolid, Spain
Javier Fresno , Dept. de Inf., Univ. de Valladolid, Valladolid, Spain
Diego R. Llanos , Dept. de Inf., Univ. de Valladolid, Valladolid, Spain
Automatic data distribution is a key feature to obtain efficient implementations from abstract and portable parallel codes. We present a highly efficient and extensible runtime library that integrates techniques for automatic data partition and mapping. It uses a novel approach to define an abstract interface and a plug-in system to encapsulate different types of regular and irregular techniques, helping to generate codes which are independent of the exact mapping functions selected. Currently, it supports hierarchical tiling of arrays with dense and stride domains, that allows the implementation of both data and task parallelism using a SPMD model. It automatically computes appropriate domain partitions for a selected virtual topology, mapping them to available processors with static or dynamic load-balancing techniques. Our library also allows the construction of reusable communication patterns that efficiently exploit MPI communication capabilities. The use of our library greatly reduces the complexity of data distribution and communication, hiding the details of the underlying architecture. The library can be used as an abstract layer for building generic tiling operations as well. Our experimental results show that the use of this library allows to achieve similar performance as carefully-implemented manual versions for several, well-known parallel kernels and benchmarks in distributed and multicore systems, and substantially reduces programming effort.
Topology, Layout, Program processors, Libraries, Arrays, Indexes
A. Gonzalez-Escribano, Y. Torres, J. Fresno and D. R. Llanos, "An Extensible System for Multilevel Automatic Data Partition and Mapping," in IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. 5, pp. 1145-1154, 2014.