Partitioning strategies: Spatiotemporal patterns of program decomposition

Abstract

We describe four partitioning strategies, or patterns, used to decompose a serial application into multiple concurrently executing parts. These partitioning strategies augment the commonly used task and data parallel patterns by recognizing that applications are spatiotemporal in nature. There fore, data and instruction decomposition are further distinguished by whether the partitioning is done in the spatial or in temporal dimension. Thus, we arrive at four decomposition strategies: spatial data partitioning (SDP), temporal data partitioning (TDP), spatial instruction partitioning (SIP), and temporal instruction partitioning (TIP), and catalog the benefits and drawbacks of each. In addition, the practical use of this work is demonstrated by applying these strategies, and combinations thereof, to implement several different parallelizations of a multicore H.264 encoder for high-definition video.