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Oracle Solaris Studio 12.3: Performance Analyzer     Oracle Solaris Studio 12.3 Information Library
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Document Information


1.  Overview of the Performance Analyzer

2.  Performance Data

3.  Collecting Performance Data

Compiling and Linking Your Program

Source Code Information

Static Linking

Shared Object Handling

Optimization at Compile Time

Compiling Java Programs

Preparing Your Program for Data Collection and Analysis

Using Dynamically Allocated Memory

Using System Libraries

Using Signal Handlers

Using setuid and setgid

Program Control of Data Collection

The C and C++ Interface

The Fortran Interface

The Java Interface

The C, C++, Fortran, and Java API Functions

Dynamic Functions and Modules



Limitations on Data Collection

Limitations on Clock-Based Profiling

Runtime Distortion and Dilation with Clock-profiling

Limitations on Collection of Tracing Data

Runtime Distortion and Dilation with Tracing

Limitations on Hardware Counter Overflow Profiling

Runtime Distortion and Dilation With Hardware Counter Overflow Profiling

Limitations on Data Collection for Descendant Processes

Limitations on OpenMP Profiling

Limitations on Java Profiling

Runtime Performance Distortion and Dilation for Applications Written in the Java Programming Language

Where the Data Is Stored

Experiment Names

Experiment Groups

Experiments for Descendant Processes

Experiments for MPI Programs

Experiments on the Kernel and User Processes

Moving Experiments

Estimating Storage Requirements

Collecting Data

Collecting Data Using the collect Command

Data Collection Options

-p option

-h counter_definition_1...[,counter_definition_n]

-s option

-H option

-M option

-m option

-S option

-c option

-I directory

-N library_name

-r option

Experiment Control Options

-F option

-j option

-J java_argument

-l signal

-t duration


-y signal [ ,r]

Output Options

-o experiment_name

-d directory-name

-g group-name

-A option

-L size

-O file

Other Options

-P process_id

-C comment





Collecting Data From a Running Process Using the collect Utility

To Collect Data From a Running Process Using the collect Utility

Collecting Data Using the dbx collector Subcommands

To Run the Collector From dbx:

Data Collection Subcommands

profile option

hwprofile option

synctrace option

heaptrace option

tha option

sample option

dbxsample { on | off }

Experiment Control Subcommands





sample record name

Output Subcommands

archive mode

limit value

store option

Information Subcommands



Collecting Data From a Running Process With dbx on Oracle Solaris Platforms

To Collect Data From a Running Process That is Not Under the Control of dbx

Collecting Tracing Data From a Running Program

Collecting Data From MPI Programs

Running the collect Command for MPI

Storing MPI Experiments

Collecting Data From Scripts

Using collect With ppgsz

4.  The Performance Analyzer Tool

5.  The er_print Command Line Performance Analysis Tool

6.  Understanding the Performance Analyzer and Its Data

7.  Understanding Annotated Source and Disassembly Data

8.  Manipulating Experiments

9.  Kernel Profiling


Where the Data Is Stored

The data collected during one execution of your application is called an experiment. The experiment consists of a set of files that are stored in a directory. The name of the experiment is the name of the directory.

In addition to recording the experiment data, the Collector creates its own archives of the load objects used by the program. These archives contain the addresses, sizes and names of each object file and each function in the load object, as well as the address of the load object and a time stamp for its last modification.

Experiments are stored by default in the current directory. If this directory is on a networked file system, storing the data takes longer than on a local file system, and can distort the performance data. You should always try to record experiments on a local file system if possible. You can specify the storage location when you run the Collector.

Experiments for descendant processes are stored inside the experiment for the founder process.

Experiment Names

The default name for a new experiment is . The suffix .er is mandatory: if you give a name that does not have it, an error message is displayed and the name is not accepted.

If you choose a name with the format, where n is a positive integer, the Collector automatically increments n by one in the names of subsequent experiments. For example, is followed by, , and so on. The Collector also increments n if the experiment already exists, and continues to increment n until it finds an experiment name that is not in use. If the experiment name does not contain n and the experiment exists, the Collector prints an error message.

Experiment Groups

Experiments can be collected into groups. The group is defined in an experiment group file, which is stored by default in the current directory. The experiment group file is a plain text file with a special header line and an experiment name on each subsequent line. The default name for an experiment group file is test.erg. If the name does not end in .erg, an error is displayed and the name is not accepted. Once you have created an experiment group, any experiments you run with that group name are added to the group.

You can manually create an experiment group file by creating a plain text file whose first line is

#analyzer experiment group

and adding the names of the experiments on subsequent lines. The name of the file must end in .erg.

You can also create an experiment group by using the -g argument to the collect command.

Experiments for Descendant Processes

Experiments for descendant processes are named with their lineage as follows. To form the experiment name for a descendant process, an underscore, a code letter and a number are added to the stem of its creator’s experiment name. The code letter is f for a fork, x for an exec, and c for combination. The number is the index of the fork or exec (whether successful or not). For example, if the experiment name for the founder process is, the experiment for the child process created by the third call to fork is . If that child process calls exec successfully, the experiment name for the new descendant process is .

Experiments for MPI Programs

Data for MPI programs are collected by default into, and all the data from the MPI processes are collected into subexperiments, one per rank. The Collector uses the MPI rank to construct a subexperiment name with the form, where m is the MPI rank. For example, MPI rank 1 would have its experiment data recorded in the directory.

Experiments on the Kernel and User Processes

Experiments on the kernel by default are named rather than When data is also collected on user processes, the kernel experiment contains subexperiments for each user process being followed.

The subexperiments are named using the format For example an experiment run on a sshd process running under process ID 1264 would be named

Moving Experiments

If you want to move an experiment to another computer to analyze it, you should be aware of the dependencies of the analysis on the operating environment in which the experiment was recorded.

The archive files contain all the information necessary to compute metrics at the function level and to display the timeline. However, if you want to see annotated source code or annotated disassembly code, you must have access to versions of the load objects or source files that are identical to the ones used when the experiment was recorded.

See How the Tools Find Source Code for a description of the process used to find an experiment's source code.

To ensure that you see the correct annotated source code and annotated disassembly code for your program, you can copy the source code, the object files and the executable into the experiment before you move or copy the experiment. You can automatically copy the load objects into the experiment using the -A copy option of the collect command or the dbx collector archive command.