Paper Info
Title
A Cluster-as-Accelerator Approach for SPMD-Free Data Parallelism
Abstract
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 high-level 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 structures. We envision coupling a simple yet powerful programming model with a lightweight, locality-aware 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.
Year
DOI
Venue
2016
10.1109/PDP.2016.97
2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)
Keywords
Field
DocType
skeletons,cluster computing,data-centric,parallel programming,skedato,exascale
Database-centric architecture,Cluster (physics),SPMD,Data analysis,Programming paradigm,Computer science,Parallel computing,Data parallelism,Software,Computer cluster,Distributed computing
Conference
ISSN
Citations 
PageRank 
1066-6192
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Maurizio Drocco18812.09
Claudia Misale2235.44
Marco Aldinucci363859.87