Concatenated Parallelism: A Technique for Efficient Parallel Divide and Conquer

Authors:
Sanjay Goil;Srinivas Aluru;Sanjay Ranka
Affiliations:
-;-;-
Venue:
SPDP '96 Proceedings of the 8th IEEE Symposium on Parallel and Distributed Processing (SPDP '96)
Year:
1996

Citing 0
Cited 4

Implementation and evaluation of an efficient parallel Delaunay triangulation algorithm

Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Parallel Formulations of Decision-Tree Classification Algorithms

Data Mining and Knowledge Discovery
References

Sourcebook of parallel computing
High performance data mining

VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Efficient divide and conquer algorithms can be mapped to a parallel computer using either Task Parallelism or Data Parallelism. The former involves significant data movement and the latter can lead to severe load imbalances. In this paper we propose a new strategy, which we call Concatenated Parallelism, for efficient parallel solution of problems resulting in divide and conquer trees. Our strategy is useful when the communication time due to data movement in distributing the subproblems is significant in comparison to the time required for subdivision.