Self-Configuring Applications for Heterogeneous Systems: Program Composition and Optimization Using Cognitive Techniques

• Published in: Proceedings of the IEEE 2008-05, Vol.96 (5), p.849-862
• Main Authors: Hall, Mary W., Gil, Yolanda, Lucas, Robert F.
• Format: Article
• Language: English
• Subjects: Commodities; Computation; Computer architecture; Computer architectures; Decisions; Devices; Distributed computing; Distributed processing; Diversity reception; Functional programming; learning systems; Mathematical models; multicore architectures; Optimization; optimizing compilers; Parallel programming; Power system management; Power system modeling; Programming; Programming profession; Resource management; self-configuring applications; Software libraries; Studies
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/JPROC.2008.917733

This paper describes several challenges facing programmers of future edge computing systems, the diverse many-core devices that will soon exemplify commodity mainstream systems. To call attention to programming challenges ahead, this paper focuses on the most complex of such architectures: integrated, power-conserving systems, inherently parallel and heterogeneous, with distributed address spaces. When programming such complex systems, new concerns arise: computation partitioning across functional units, data movement and synchronization, managing a diversity of programming models for different devices, and reusing existing legacy and library software. We observe that many of these challenges are also faced in programming applications for large-scale heterogeneous distributed computing environments, and current solutions as well as future research directions in distributed computing can be adapted to commodity computing environments. Optimization decisions are inherently complex due to large search spaces of possible solutions and the difficulty of predicting performance on increasingly complex architectures. Cognitive techniques are well suited for managing systems of such complexity, citing recent trends of using cognitive techniques for code mapping and optimization support. Combining these, we describe a fundamentally new programming paradigm for complex heterogeneous systems, where programmers design self-configuring applications and the system automates optimization decisions and manages the allocation of heterogeneous resources.