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Oracle Solaris Studio 12.3: Performance Analyzer Oracle Solaris Studio 12.3 Information Library |
1. Overview of the Performance Analyzer
3. Collecting Performance Data
Compiling and Linking Your Program
Preparing Your Program for Data Collection and Analysis
Using Dynamically Allocated Memory
Program Control of Data Collection
The C, C++, Fortran, and Java API Functions
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
Estimating Storage Requirements
Collecting Data Using the collect Command
-h counter_definition_1...[,counter_definition_n]
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:
Experiment Control 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
4. The Performance Analyzer Tool
5. The er_print Command Line Performance Analysis Tool
6. Understanding the Performance Analyzer and Its Data
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.
The default name for a new experiment is test.1.er . 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 experiment.n.er, where n is a positive integer, the Collector automatically increments n by one in the names of subsequent experiments. For example, mytest.1.er is followed by mytest.2.er, mytest.3.er , 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.
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 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 test.1.er, the experiment for the child process created by the third call to fork is test.1.er/_f3.er . If that child process calls exec successfully, the experiment name for the new descendant process is test.1.er/_f3_x1.er .
Data for MPI programs are collected by default into test.1.er, 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 M_rm.er, where m is the MPI rank. For example, MPI rank 1 would have its experiment data recorded in the test.1.er/M_r1.er directory.
Experiments on the kernel by default are named ktest.1.er rather than test.1.er. 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 _process-name_PID_process-id.1.er. For example an experiment run on a sshd process running under process ID 1264 would be named ktest.1.er/_sshd_PID_1264.1.er.
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.