A Cluster-as-Accelerator Approach for SPMD-Free Data Parallelism

Maurizio Drocco, Claudia Misale, Marco Aldinucci
2016 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)  
In this paper we present a novel approach for functional-style programming of distributed-memory clusters, targeting data-centric applications. The programming model proposed is purely sequential, SPMD-free and based on highlevel functional features introduced since C++11 specification. Additionally, we propose a novel cluster-as-accelerator design principle. In this scheme, cluster nodes act as general interpreters of user-defined functional tasks over node-local portions of distributed data
more » ... ructures. We envision coupling a simple yet powerful programming model with a lightweight, localityaware distributed runtime as a promising step along the road towards high-performance data analytics, in particular under the perspective of the upcoming exascale era. We implemented the proposed approach in SkeDaTo, a prototyping C++ library of data-parallel skeletons exploiting cluster-as-accelerator at the bottom layer of the runtime software stack.
doi:10.1109/pdp.2016.97 dblp:conf/pdp/DroccoMA16 fatcat:bbbvx77tcbhhbhrtdtppjltyfy