Smart-DASH: Smart Data Structures and Algorithms with Support for Hierarchical Locality

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We propose Smart-DASH, a continuation and extension of our work with DASH, with which we would like to make a contribution towards efficient data-centric programming on current and upcoming parallel architectures. Smart-DASH builds upon our previous work with DASH, a data-structure oriented C++ template library that can exploit hierarchical organization afforded by machines and algorithms. Smart-DASH extends this work by addressing some of the most pressing challenges on the way to Exascale. The runtime and C++ template library will be extended to support a task-based execution model and with the notion of memory spaces, Smart-DASH will be able to exploit the complex memory architecture of upcoming machines. Several case studies will explore the utility of these new features in the context of important scientific problem classes. We will develop smart data structures that capture frequently encountered application scenarios such as N-dimensional arrays with built-in support for halo regions to enable a productive transition onto new hardware platforms and assist in code modernization efforts. To address fault-tolerance and reliability, we will explore concepts for the redundant storage of data items and with the DASH data dock we will explore the usage of the PGAS approach in general and NVRAM in particular for the coupling of applications. We will test this approach in the context of a real-world astro-particle application.