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Project Description

Report potential performance issues and possible remedies for a given executable.

SCA will non-destructively instrument the executable and run the instrumented version, collecting performance data. Upon completion, a report will be presented listing any detected issues with possible remedies.

IMPORTANT: To use SCA the binary must be linked with relocation information preserved, which is not the default linker behavior. Add "-q" or "--preserve-relocs" to the "ld" command or "-Wl,-q" or "-Wl,--preserve-relocs" if the compiler is used to link."

Source Code Advisor (SCA) and Feedback-Directed Program Restructuring (FDPR) work together to allow you to analyze and optimize your applications.

FDPR works similarly to a compiler: it reads a linked executable program and produces an optimized version of it. Both regular executable and shared library forms are supported. The optimization uses an execution profile, collected by running an instrumented version of the input.

During the code optimization process, FDPR produces a journal of the optimizations performed. The Source Code Advisor uses this journal, produced as an XML file, to highlight potential problems in your source code and to offer suggested solutions. The journal explains each optimization site, including the source location, execution count, the performance problem found, and the user action required to resolve the problem. It is important to select a representative workload for both SCA and for FDPR so that the optimization step is effective.

Because SCA uses information gathered by FDPR, knowledge of this tool is important.

The combination of SCA and FDPR provide you with two major approaches to performance analysis and optimization:

  • Find and visualize performance problems in the source program using feedback-directed analysis.

  • Perform feedback-directed optimization of an executable program (or a shared library).

For more information about SCA usage, see sca --help

Contributing to the project

We welcome contributions to the Source Code Advisor Project in many forms. There's always plenty to do! Full details of how to contribute to this project are documented in the CONTRIBUTING.md file.

Maintainers

The project's maintainers: are responsible for reviewing and merging all pull requests and they guide the over-all technical direction of the project.

Communication

We use Slack for communication.

Supported Architecture and Operating Systems

ppc64le: Ubuntu 16.04, CentOS7, RHEL 7.3, SLES12, Fedora 25.

Installing

Requirements: python-pip, python-pylint, python-virtualenv, python-docsutil, fdprpro and fdprwrap

Testing: ./dev tests

Build: ./dev release

Build and install: ./dev install

Execution: sca --help

FDPRPro and FDPRWrap

Both are proprietary tools by IBM which are freely available at https://developer.ibm.com/linuxonpower/sdk-packages/. Ensure you have both tools installed in order to execute Source Code Advisor.

Documentation

usage: sca [-h] [--version] [--color] [--fdprpro-args [FDPRPRO_ARGS]] [--output-type [{txt,json}]] [--output-name [FILE_NAME]] COMMAND

COMMAND the application and its arguments e.g.: sca

-h, --help show this help message and exit

--version, -V show program's version number and exit

--color displays results in color

--fdprpro-args [FDPRPRO_ARGS] fdprpro options e.g.: --fdprpro-args='-O3 -v 3' To get all available options for fdprpro issue: /opt/ibm/fdprpro/bin/fdprpro --help

--output-type [{txt,json}] The output of the report file e.g.:--output-type=txt

--output-name [FILE_NAME] The name of the report file e.g.: --output-name=file_name

Still Have Questions?

For general purpose questions, please use StackOverflow.

License

The Source Code Advisor Project uses the Apache License Version 2.0 software license.

Related information

[FDPR] (https://www.research.ibm.com/haifa/projects/systems/cot/fdpr/)

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Non-destructively instrument a C/C++ executable and run it and collect performance data. Upon completion, a report will be presented listing any detected issues with possible remedies.

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  • Python 92.3%
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