Heterogeneous distributed computing based on high‐level abstractions. (2nd August 2018)

• Record Type: Journal Article
• Authors: Viñas, M.; Fraguela, B. B.; Andrade, D.; Doallo, R.
• Other Names: Aupy Guillaume guestEditor.; Tang Xueyan guestEditor.; Fröning Holger guestEditor.; Silla Federico guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 17(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-02
• Subjects: data parallelism -- distributed computing -- heterogeneous computing -- OpenCL -- programming model -- runtime system; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4664
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The rise of heterogeneous systems has given place to great challenges for users as they involve new concepts, restrictions, and frameworks. Their exploitation is further complicated in the context of distributed memory systems, which require the usage of additional different programming paradigms and tools. In this paper, we propose a novel approach to program heterogeneous clusters that is based on high‐level abstractions such as tiles and hierarchical decomposition combined with the powerful APIs that data types and embedded languages can provide in languages such as C++. Rather than building our proposal from scratch, we have implemented it as a natural integration of the existing Hierarchically Tiled Arrays (HTA) and Heterogeneous Programming Library (HPL) projects, ie, the first one being focused on distributed computing and the second one on heterogeneous processing. The result, called Heterogeneous Hierarchically Tiled Arrays (H 2 TA), is very intuitive and easy to use thanks to the global view of the data and the single‐threaded view of the execution that it provides at cluster level together with the transparency it provides with respect to the management of the heterogeneous devices. An evaluation comparing our proposal with MPI‐based implementations shows its large programmability advantages and the reasonable overhead incurred.