@TechReport{	  it:2014-010,
  author	= {Martin Tillenius},
  title		= {{SuperGlue}: A Shared Memory Framework using
		  Data-Versioning for Dependency-Aware Task-Based
		  Parallelization},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Scientific Computing},
  year		= {2014},
  number	= {2014-010},
  month		= apr,
  abstract	= {In computational science, making efficient use of modern
		  multicore based computer hardware is necessary in order to
		  deal with complex real-life application problems. However,
		  with increased hardware complexity, the cost in man hours
		  of writing and re-writing software to adapt to evolving
		  computer systems is becoming prohibitive. Task based
		  parallel programming models aim to allow the application
		  programmers to focus on the algorithms and applications,
		  while the performance is handled by a runtime system that
		  schedules the tasks onto nodes, cores, and accelerators. In
		  this paper we describe a task parallel programming model
		  where dependencies are represented through data versioning.
		  The benefits of using this type of model are that it is
		  easy to represent different types of dependencies and that
		  scheduling decisions can be made locally. We implement this
		  as a freely available C++ header-only template library, and
		  show experimental results indicating that our
		  implementation scales well, and performs well in comparison
		  to other related efforts.}
}