Abstract
Global is an optimization algorithm conceived in the ’80s. Since then several papers discussed improvements of the algorithm, but adapting it to a multi-thread execution environment is only a recent branch of development [1]. Our previous work focused on parallel implementation on a single machine but sometimes the use of distributed systems is inevitable. In this paper we introduce a new version of Global which is the first step towards a fully distributed algorithm. While the proposed implementation still works on a single machine, it is easy to see how gossip based information sharing can be built into and be utilized by the algorithm. We show that ParallelGlobal is a feasible way to implement Global on a distributed system. However, further improvements must be made to solve real world problems with the algorithm.
Highlights
Global is an optimization algorithm built from multiple modules working in an ensemble
To speed up optimization processes we developed an algorithm [1] that is capable of utilizing multiple computational threads of a single machine
It cannot be directly implemented for distributed systems as the millisecond order of magnitude latency in communication would significantly slow down the synchronization of threads. To mitigate this problem we propose ParallelGlobal, a parallel implementation suitable for distributed systems with high latency or even with unreliable communication channels
Summary
Global is an optimization algorithm built from multiple modules working in an ensemble. To speed up optimization processes we developed an algorithm [1] that is capable of utilizing multiple computational threads of a single machine. It cannot be directly implemented for distributed systems as the millisecond order of magnitude latency in communication would significantly slow down the synchronization of threads. In this paper we introduce an experimental version whose main purpose is to test the feasibility of the proposed solution. It provides an algorithm skeleton for a real distributed implementation
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