Name
Abstract | ||
|---|---|---|
Recent publications have emphasised map-reduce as a general programming model (labelled Google map-reduce), and described existing high-performance implementations for large data sets. We present two parallel implementations for this Google map-reduce skeleton, one following earlier work, and one optimised version, in the parallel Haskell extension Eden. Eden's specific features, like lazy stream processing, dynamic reply channels, and nondeterministic stream merging, support the efficient implementation of the complex coordination structure of this skeleton. We compare the two implementations of the Google map-reduce skeleton in usage and performance, and deliver runtime analyses for example applications. Although very flexible, the Google map-reduce skeleton is often too general, and typical examples reveal a better runtime behaviour using alternative skeletons. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03869-3_91 | Euro-Par |
Keywords | Field | DocType |
runtime analysis,parallel implementation,nondeterministic stream,emphasised map-reduce,parallel google map-reduce,parallel haskell extension,alternative skeleton,lazy stream processing,labelled google map-reduce,general programming model,google map-reduce skeleton,generic programming,stream processing | Programming language,Computer science,Algorithmic skeleton,Implementation,Theoretical computer science,Haskell,Merge (version control),Distributed computing,Nondeterministic algorithm,Programming paradigm,Parallel computing,Communication channel,Stream processing | Conference |
Volume | ISSN | Citations |
5704 | 0302-9743 | 6 |
PageRank | References | Authors |
0.49 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jost Berthold | 1 | 123 | 10.76 |
| Mischa Dieterle | 2 | 44 | 3.41 |
| Rita Loogen | 3 | 598 | 42.21 |