Tool Mentor: Finding Performance Bottlenecks Using Rational Quantify and Rational PurifyPlus (UNIX)

Tool Mentors > Rational Quantify Tool Mentors > Finding Performance Bottlenecks Using Rational Quantify and Rational PurifyPlus (UNIX)

Purpose

This tool mentor provides an overview of how to use Rational Quantify® to quickly pinpoint performance bottlenecks in your programs. PurifyPlus is a Rational product that includes Quantify functionality.

To learn more about Quantify and for step-by-step instructions, read the Installing and Getting Started manual for the PurifyPlus product family (UNIX version).

For step-by-step instructions for using Quantify and for reference information, see Quantify online Help.

Related Rational Unified Process information:

Activity: Perform Unit Tests, refer to the section titled "Execute Unit Test".
Activity: Analyze Execution Failures, refer to the section titled "Evaluate Execution of Test"
Activity: Determine Test Results, refer to the section titled "Evaluate Code-based Test Coverage"

Overview

Quantify provides a complete, accurate, and easy-to-interpret set of performance data for your program and its components (including threads), so that you can identify and eliminate performance bottlenecks in your code.

Tool Steps

To profile a program's performance:

Run a program under Quantify to collect performance data
Analyze the performance data
Eliminate bottlenecks and rerun to verify improvements

1. Run a program under Quantify to collect performance data

The first step in improving your program's performance is to collect performance data.

Just add the word quantify to the beginning of your compile/link line. Quantify collects performance data at the function level and, if you compile with the -g debugging option, at the line level. For example:

% quantify cc -g hello_world.c

Quantify makes a copy of your object code and libraries, and instruments it using Object Code Insertion (OCI) technology to insert instructions for collecting performance data. When instrumentation is complete, run the instrumented program. For example:

% a.out

As you exercise your code, Quantify records data about its performance. You can call Quantify API functions from your program to pause and resume data recording at any time, so that you can concentrate on specific portions of code.

When you exit the program, Quantify displays the Quantify Control Panel, from which you can access its data analysis windows and begin analyzing your program's performance.

Tip: In addition to using Quantify interactively, you can also use it with your test scripts, makefiles, and batch files for automated testing. View output from your tests using the command:
qv a.out.<process id>.<sequence number>.qv

For more information, look up the following topics in the Quantify online Help index:

basic steps
scripts
options
API functions

2. Analyze the performance data

The second step in improving your program's performance is to analyze the performance data that Quantify has collected.

From the Quantify Control Panel, which is displayed when you exit the program for which you have been collecting data, you can open the Call Graph window or the Function List window to examine the data in different formats. From these windows you can open the Function Detail window and the Annotated Source window.

The Call Graph window displays a schematic representation of the calling structure and performance of the functions in the program. By default, the call graph displays the 20 functions that contributed most to the overall time of the program. As you examine your program's performance, you can show additional functions, hide functions, and focus on the specific functions and their descendants that are most critical for your work.

The Function List window displays in table format the statistics for the functions. You can sort them on the basis of a large number of criteria, such as the time the program spends in the function, the time the program spends in the function and its descendants, and the number of calls the function makes.

The Function Detail window displays data for a specific function, and data about its callers and descendants.

The Annotated Source window displays line-by-line performance data on a copy of your source code. This is available if you compiled with the -g debugging option.

Quantify's results include virtually no overhead from the profiling process itself. The numbers you see are the time your program would take without Quantify.

With the data you collect, you will be able to identify performance bottlenecks such as needless computations or recomputations, premature computations, or excessive and expensive library calls.

For more information, look up the following topics in the Quantify online Help index:

call graph
function list
function detail
annotated source
bottlenecks

3. Eliminate bottlenecks and rerun to verify improvements

The third and final step in improving your program's performance is to modify your code to eliminate bottlenecks, and then compare "before" and "after" performance data to see whether the modifications have caused the performance to improve or regress.

After you modify your code, you can rerun the updated program and use Quantify's qxdiff script to compare the new results to any previous run.

You can save performance data as a Quantify data file (.qv) to use for further analysis or to share with other Quantify users. You can save data in export format for use outside of Quantify.

For more information, look up the following topics in the Quantify online Help index:

qxdiff
saving data

Rational Unified Process