Abstract

A visualization model has been developed to analyze the performance of a massively parallel algorithm. Most visualization tools that have been developed so far for performance analysis are based generally on individual processor information and communication patterns (e.g., processor load, message traffic, etc.). However, these tools are inadequate for massively parallel computations. It is difficult to comprehend the visual information for many processors. The scientific visualization in multicomputing for interpretation of large amounts of information (SMILI) model addresses this problem by using abstract representations to attain a composite picture, which gives better insight to the behavior of the algorithm. Chernoff's Faces have been selected to represent the multidimensional data because of their ability to portray multidimensional data in a very perceptive manner. SMILI has been used on an asynchronous massively parallel partial differential equation solver based on the multigrid paradigm. The visualization tool helps in tuning the control parameters of the multigrid algorithm to get optimal results. In asynchronous algorithms, the non-deterministic way in which messages are exchanged may lead to some unforeseeable behavior. SMILI helps detect the anomalies and indicate the causes of irregularities that may arise during execution. Once the causes have been determined, the control parameters can be further tuned to eliminate the erroneous behavior in the consecutive executions.

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