DASH is a C++ Template Library for Distributed Data
Structures with Support for Hierarchical Locality for HPC and
Data-Driven Science.
Exascale systems are scheduled to become available in the coming
years 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.
