Introduction

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   Table of Contents

   1. Introduction

   2. Installation

   3. Using PerfExplorer

1. Introduction

   PerfExplorer is a framework for parallel performance data mining and
   knowledge discovery. The framework architecture enables the development
   and integration of data mining operations that will be applied to
   large-scale parallel performance profiles.

   The overall goal of the PerfExplorer project is to create a software to
   integrate sophisticated data mining techniques in the analysis of
   large-scale parallel performance data.

   PerfExplorer supports clustering, summarization, association, regression,
   and correlation. Cluster analysis is the process of organizing data points
   into logically similar groupings, called clusters. Summarization is the
   process of describing the similarities within, and dissimilarities
   between, the discovered clusters. Association is the process of finding
   relationships in the data. One such method of association is regression
   analysis, the process of finding independent and dependent correlated
   variables in the data. In addition, comparative analysis extends these
   operations to compare results from different experiments, for instance, as
   part of a parametric study.

2. Installation

   PerfExplorer uses PerfDMF databases so if you have not already you will
   need to install PerfDMF. To configure PerfExplorer move to the
   tools/src/perfexplorer/ directory in you TAU distribution. Type:

 %>./configure

   Next add [path to tau]/tau2/apple/bin to your path. Now type:

 %>make

3. Using PerfExplorer

   To run PerfExplorer type:

 %>perfexplorer

   When PerfExplorer loads you will see on the left window all the
   experiments that where loaded into PerfDMF. You can select which
   performance data you are interested by navigating the tree structure.
   PerfExplorer will allow you to run analysis operations on these
   experiments. Also the cluster analysis results are visible on the right
   side of the window. Various types of comparative analysis are available
   from the drop down menu selected.

   To run an analysis operation, first select the metric of interest form the
   experiments on the left. Then perform the operation by selecting it from
   the Analysis menu. If you would like you can set the clustering method,
   dimension reduction, normalization method and the number of clusters from
   the same menu.

   The options under the Charts menu provide analysis over an entire trial.
   To view these charts first choose a metric of interest by selecting a
   trial form the tree on the left. Then choose the Set Metric of Interest or
   Set Event of Interest form the Charts menu. Now you can view a chart by
   selecting it from the Charts menu.
