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Scalability of communicators in MPI

University of British Columbia

This thesis offers a novel framework for representing groups and communicators in Message Passing Interface (MPI) middleware. MPI is a widely used paradigm in a cluster environment that supports communication between the nodes. In our framework, we have implemented and evaluated scalable techniques for groups and communicators in MPI. We have tested this framework using FG-MPI, a fine-grain version of MPI that scales millions of MPI processes. Groups in MPI are the primary means for creating communicators. A group map is the underlying structure that stores participating processes in the communication. We introduce a framework for concise representations of the group map. This framework is based on the observation that a map can be decomposed into a set and a permutation. This decomposition allows us to use a compact set representation for the cases where specific mapping is not required i.e. lists with monotonically increasing order. In other cases, the representation adds a permutation as well. A variety of set compression techniques has been used. Furthermore, the framework is open to integration of new representations. One advantage of such decomposition is the ability to implicitly represent a set with set representations such as BDD. BDD and similar representations are well-suited for the types of operations used in construction of communicators. In addition to set representations for unordered maps, we incorporated Wavelet Trees on Runs. This library is designed to represent permutation. We have also included general compression techniques in the framework such as BWT. This allows some degree of compression in memory-constrained environments where there is no discernible pattern in the group structure. We have investigated time and space trade-offs among the representations to develop strategies available to the framework. The strategies tune the framework based on user's requirements. The first strategy optimizes the framework to be fast and is called the time strategy. The second strategy optimizes the framework in regard to space. The final hybrid strategy is a hybrid of both and tries to strike a reasonable trade-off between time and space. These strategies let the framework accommodate a wider range of applications and users.

Text

2011-03-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/33128

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/