About DASHDASH is a C++ Template Library for Distributed Data Structures with Support for Hierarchical Locality for HPC and Data-Driven Science. The DASH library is cur rently unter develpment and a first version should be available later this year
HPC Workshop 2014New: The DASH project is organizing an introductory HPC workshop for life-science researchers in September 2014, co-located with the GridKa School in Karlsruhe. The workshop specifically targets female students and early-career scientists in the life-sciences, health and bio-sciences, and travel and registration stipends are available for attending the workshop. Please consult this website for more information.
FundingDASH is funded by the German Research Foundation (DFG) under the priority programme "Software for Exascale Computing - SPPEXA" (2013-2015).
Exascale systems are scheduled to become available in 2018-2020 and will be characterized by extreme scale and a multilevel hierarchical organization. Efficient and productive programming of these systems will be a challenge, especially in the context of data-intensive applications. Adopting the promising notion of Partitioned Global Address Space (PGAS) programming the DASH project develops a data-structure oriented C++ template library that provides hierarchical PGAS-like abstractions for important data containers (multidimensional arrays, lists, hash tables, etc.) and allows a developer to control (and explicitly take advantage of) the hierarchical data layout of global data structures. In contrast to other PGAS approaches such as UPC, DASH does not propose a new language or require compiler support to realize global address space semantics. Instead, operator overloading and other advanced C++ features are used to provide the semantics of data residing in a global and hierarchically partitioned address space based on a runtime system with one-sided messaging primitives provided by MPI or GASNet. As such, DASH can co-exist with parallel programming models already in widespread use (like MPI) and developers can take advantage of DASH by incrementally replacing existing data structures with the implementation provided by DASH. Efficient I/O directly to and from the hierarchical structures and DASH-optimized algorithms such as map-reduce are also part of the project. Two applications from molecular dynamics and geoscience are driving the project and are adapted to use DASH in the course of the project.