Workshop: IA^3 2022 - 12th Workshop on Irregular Applications: Architectures and Algorithms
Authors: Brian Page (University of Maryland, Laboratory of Physical Sciences (LPS)) and Peter Kogge (University of Notre Dame)
Abstract: The conventional model of parallel programming today involves either copying data (and then having to track its most recent value), or not copying and requiring deep software stacks to do even the simplest operation on data that is "over there" - out of the range of loads and stores from the current core. As applications require larger data sets, with more irregular access to them, both models begin to exhibit severe scaling problems. This presentation reviews some growing evidence of the potential value of a model of computation that skirts between the two: data does not move (i.e. is not copied), and computation instead moves to the data. Several different applications have been coded for a novel platform where thread movement is handled invisibly by the hardware. The evidence to date indicates that parallel scaling for this paradigm may very well be significantly better than any mix of conventional models.
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