Table of Contents
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.
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.