Chapter 2. Profiling

This chapter describes running an instrumented application, generating profile data and analyzing that data. Profiling shows the summary statistics of performance metrics that characterize application performance behavior. Examples of performance metrics are the CPU time associated with a routine, the count of the secondary data cache misses associated with a group of statements, the number of times a routine executes, etc.

2.1. Running the Application

After instrumentation and compilation are completed, the profiled application is run to generate the profile data files. These files can be stored in a directory specified by the environment variable PROFILEDIR. By default, profiles are placed in the current directory. You can also set the TAU_VERBOSE enviroment variable to see the steps the TAU measurement systems takes when your application is running. Example:

% setenv TAU_VERBOSE 1
% setenv PROFILEDIR /home/sameer/profiledata/experiment55
% mpirun -np 4 matrix 

Other environment variables you can set to enable these advanced MPI measurement features are TAU_TRACK_MESSAGE to track MPI message statistics when profiling or messages lines when tracing, and TAU_COMM_MATRIX to generate MPI communication matrix data.