Fine-grained parallel traversals of irregular data structures

Authors:
Bin Ren;Gagan Agrawal;James R. Larus;Todd Mytkowicz;Tomi Poutanen;Wolfram Schulte
Affiliations:
The Ohio State University, Columbus, OH, USA;The Ohio State University, Columbus, OH, USA;Microsoft Research, Redmond, WA, USA;Microsoft Research, Redmond, WA, USA;Microsoft, Redmond, WA, USA;Microsoft Research, Redmond, WA, USA
Venue:
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Year:
2012

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Abstract

Fine-grain data parallelism is increasingly common in mainstream processors in the form of long vectors and on-chip GPUs. This paper develops compiler and runtime support to exploit such data parallelism for non-numeric, non-graphic, irregular parallel tasks that perform simple computations while traversing many independent, irregular data structures, like trees and graphs. We vectorize the traversal of trees and graphs by treating a set of irregular data structures as a parallel control-flow graph and compiling the traversal into a domain-specific bytecodes. We produce a SIMD interpreter for these bytecodes, so each lane of a SIMD unit traverses one irregular data structure. Despite the overhead of interpretation, we demonstrate significant increases in single-core performance over optimized baselines.