Algorithmic skeletons: structured management of parallel computation
Algorithmic skeletons: structured management of parallel computation
Programming parallel algorithms
Communications of the ACM
Design Patterns: Abstraction and Reuse of Object-Oriented Design
ECOOP '93 Proceedings of the 7th European Conference on Object-Oriented Programming
Patterns and skeletons for parallel and distributed computing
Patterns and skeletons for parallel and distributed computing
Parallel functional programming in Eden
Journal of Functional Programming
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Google's MapReduce programming model – Revisited
Science of Computer Programming
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Patterns for parallel programming
Patterns for parallel programming
Map-reduce as a Programming Model for Custom Computing Machines
FCCM '08 Proceedings of the 2008 16th International Symposium on Field-Programmable Custom Computing Machines
Higher-Order and Symbolic Computation
Static type checking of Hadoop MapReduce programs
Proceedings of the second international workshop on MapReduce and its applications
Skeleton composition using remote data
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
Functional high performance financial IT: the hiperfit research center in copenhagen
TFP'11 Proceedings of the 12th international conference on Trends in Functional Programming
Eden --- parallel functional programming with haskell
CEFP'11 Proceedings of the 4th Summer School conference on Central European Functional Programming School
| Hi-index | 0.00 |
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.