An automated approach for estimating the memory footprint of nonlinear data objects. (25th July 2014)

• Record Type: Journal Article
• Authors: Dreßler, Sebastian; Steinke, Thomas
• Other Names: Drira Khalil guestEditor.; Reddy Sumitra guestEditor.; Bientinesi Paolo guestEditor.; Herrero José R. guestEditor.; Quintana‐Ortí Enrique S. guestEditor.; Strzodka Robert guestEditor.
• Is Part Of: Concurrency and computation. Volume 27:Number 6(2015:Apr.)
• Page Start: 1564
• Page End: 1574
• Publication Date: 2014-07-25
• Subjects: data structures -- heterogeneous architectures -- static and dynamic object analysis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3345
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Current programming models for heterogeneous devices with disjoint physical memory spaces require explicit allocation of device memory and explicit data transfers. While it is quite easy to manually implement these operations for linear data objects like arrays, this task becomes more difficult for nonlinear objects, for example, linked lists or multiple inherited classes. The difficulties arise because of memory requirements known only at run‐time and the composition of complex data structures from basic types. In this paper, we present a novel method to build a graph‐based static data‐type description that is used to create code for injectable functions that automatically determine the memory footprint of data objects at run‐time. Our approach is extensible to implement automatically generated optimized data transfers across physical memory spaces. Copyright © 2014 John Wiley & Sons, Ltd.