DIVIDE: distributed visual display of the execution of asynchronous, distributed algorithm on loosely-coupled parallel processors

• Authors: Tom M. Morrow;Sumit Ghosh
• Venue: VIS '93 Proceedings of the 4th conference on Visualization '93
• Year: 1993

The issue of monitoring the execution of asynchronous, distributed algorithms on loosely-coupled parallel processor systems, is important for the purposes of (i) detecting inconsistencies and flaws in the algorithm, (ii) obtaining important performance parameters for the algorithm, and (iii) developing a conceptual understanding of the algorithm's behavior, for given input stimulus, through visualization. For a particular class of asynchronous distributed algorithms [1,5] that may be characterized by independent and concurrent entities that execute asynchronously on multiple processors and interact with one another through explicit messages, the following reasoning applies. Information about the flow of messages and the activity of the processors may contribute significantly towards the conceptual understanding of the algorithm's behavior and the functional correctness of the implementation. The computation and subsequent display of important parameters, based upon the execution of the algorithm, is an important objective of DIVIDE. For instance, the mean and standard deviation values for the propagation delay of ATM cells between any two given Broadband-ISDN (BISDN) nodes in a simulation of BISDN network under stochastic input stimulus[36], as a function of time, are important clues to the degree of congestion in the Broadband-ISDN network. Although the execution of the algorithm typically generates high resolution data, often, a coarse-level visual representation of the data may be useful in facilitating the conceptual understanding of the behavior of the algorithm. DIVIDE permits a user to specify a resolution less than that of the data from the execution of the algorithm, which is then utilized to coalesce the data appropriately. Given that this process requires significant computational power, for efficiency, DIVIDE distributes the overall task of visual display into a number of user specified workstations that are configured as a loosely-coupled parallel processor. DIVIDE has been implemented on a heterogeneous network of SUN sparc 1+, sparc 2, and 3/60 workstations and performance measurements indicate significant improvement over that of a uniprocessor-based visual display.