@TechReport{	  it:2017-008,
  author	= {Mahdad Davari and Erik Hagersten and Stefanos Kaxiras},
  title		= {Scope-Aware Classification: Taking the Hierarchical
		  Private/Shared Data Classification to the Next Level},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Computer Systems},
  year		= {2017},
  number	= {2017-008},
  month		= apr,
  abstract	= {Hierarchical techniques are commonplace in ameliorating
		  the bottlenecks, such as cache coherence, in the design of
		  scalable multi/manycores. Furthermore, there have been
		  proposals to simplify the coherence based on the
		  data-race-free semantics of the software and private/shared
		  data classification, where cores self-invalidate their
		  shared data upon synchronizations. However, naive
		  private/shared data classification in the hierarchies
		  nullifies such optimizations by increasing the amount of
		  data misclassified as shared and therefore being needlessly
		  self-invalidated.
		  
		  We introduce a private/shared data classification approach
		  for hierarchical clusters, where a datum is concurrently
		  classified as private and shared with respect to different
		  classification scopes. Such scope-aware classification
		  eliminates the needless self-invalidation of the valid data
		  at synchronizations, resulting in a coherence scheme that
		  reduces the average network traffic and execution time by
		  30\% and 5\%, respectively. }
}