Paper Info
Title
Data-only flattening for nested data parallelism
Abstract
Data parallelism has proven to be an effective technique for high-level programming of a certain class of parallel applications, but it is not well suited to irregular parallel computations. Blelloch and others proposed nested data parallelism (NDP) as a language mechanism for programming irregular parallel applications in a declarative data-parallel style. The key to this approach is a compiler transformation that flattens the NDP computation and data structures into a form that can be executed efficiently on a wide-vector SIMD architecture. Unfortunately, this technique is ill suited to execution on today's multicore machines. We present a new technique, called data-only flattening, for the compilation of NDP, which is suitable for multicore architectures. Data-only flattening transforms nested data structures in order to expose programs to various optimizations while leaving control structures intact. We present a formal semantics of data-only flattening in a core language with a rewriting system. We demonstrate the effectiveness of this technique in the Parallel ML implementation and we report encouraging experimental results across various benchmark applications.
Year
DOI
Venue
2013
10.1145/2442516.2442525
PPOPP
Keywords
Field
DocType
parallel application,data-only flattening,ndp computation,effective technique,parallel computation,nested data parallelism,nested data structure,data parallelism,data structure,new technique,flattening,compilers,multicore
Data structure,Programming language,Implicit parallelism,NESL,Computer science,Task parallelism,Parallel computing,Compiler,Data parallelism,Rewriting,Multi-core processor
Conference
Volume
Issue
ISSN
48
8
0362-1340
Citations 
PageRank 
References 
10
0.49
13
Authors
6
Name
Order
Citations
PageRank
Lars Bergstrom1815.23
Matthew Fluet229620.32
Mike Rainey318310.69
John H. Reppy489984.36
Stephen Rosen5150.94
Adam Shaw6934.77