In this paper we introduce a new self-organizing neural network, the Kohonen Network Incorporating Explicit Statistics (KNIES) that is based on Kohonen's Self-Organizing Map (SOM). The primary difference between the SOM and the KNIES is the fact that every iteration in the training phase includes tw
The dance party problem and its application to collective communication in computer networks
β Scribed by Xin Wang; Edward K. Blum; D.Stott Parker; Daniel Massey
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 996 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0167-8191
No coin nor oath required. For personal study only.
β¦ Synopsis
Motivated by implementing collective communication operations on workstation clusters, a problem of scheduling a dance party is formulated. The problem is solved by two algorithms based on searching and divide-and-conquer that generate suboptimal schedules and an algorithm based on graph factorization that generates optimal schedules. It is shown how to use dance schedules to implement collective communication operations such as all -gather. Experiment data on a single ethernet segment of SUN SPARC-10 workstations and on a switch connected network, the IBM SP2, show that the all -gather implementation that simply uses the optimal schedule performs better for long messages than the implementations of system MPL from IBM and public-domain systems LAM and MPICH/pA
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