Load Balancing Algorithms with Partial Information Management for the DLML Library

Load balancing algorithms are an essential component of parallel computing reducing the response time of applications. Frequently, balancing algorithms have a centralized behavior requiring a lot of messages to operate, thus causing scalability problems. A solution to improve scalability is to define a decentralized algorithm, avoiding the generation of bottlenecks. DLML (Data List Management Library) is a tool that, in a transparent way, allows the parallel processing of data that are organized through a List. One drawback of this tool is the global bidding algorithm used to distribute the data (work) generated during the execution. In this paper two load balancing algorithms for DLML handling partial information are proposed. The first algorithm considers a logical Torus topology and the second one follows a Binary Tree topology for communications. Results show how the scalability of DLML was improved, using two clusters of 40 and 1024 processing units, and executing dynamic and static applications.