commit | 3501cc7eec7b53364a5f325c6e7ecba883e716ed | [log] [tgz] |
---|---|---|
author | Patrick Bellasi <patrick.bellasi@arm.com> | Fri Jul 15 14:21:08 2016 +0100 |
committer | Patrick Bellasi <patrick.bellasi@arm.com> | Fri Jul 15 20:07:25 2016 +0100 |
tree | b0dd3ca083ef9a41b3671977ecdeddaccccc9dfa | |
parent | 79a153ae2cea469af80c9d5324c479365980a3ad [diff] |
libs/utils/trace_analysis: add plot for functions profiling data Since this commit: 082a82c7c ftrace: add support to report function profiling data devlib provides support to profile a list of specified kernel functions by specifying them in the TestEnv configurations, e.g. my_conf = { #... other settings ... "ftrace" : { "functions" : [ "select_task_rq_fair", "pick_next_task_fair", ], }, } With such a configuration, functions profiling data can be collected using this API: # Collect and keep track of the performance data stats_file = os.path.join(te.res_dir, 'trace.stats') te.ftrace.get_stats(stats_file) This patch adds the required support from the LISA side to plot the data related to functions execution times whenever available in the output folder. Signed-off-by: Patrick Bellasi <patrick.bellasi@arm.com>
NOTE: This is still a work in progress project, suitable for: developers, contributors and testers.
None of the provided tests should be considered stable and/or suitable for the evaluation of a product.
The LISA project provides a toolkit that supports regression testing and interactive analysis of workload behavior. LISA stands for Linux Integrated/Interactive System Analysis. LISA's goal is to help Linux kernel developers to measure the impact of modifications in core parts of the kernel. The focus is on the scheduler, power management and thermal frameworks. However LISA is generic and can be used for other purposes too.
LISA provides an API for modeling use-cases of interest and developing regression tests for use-cases. A ready made set of test-cases to support regression testing of core kernel features is provided. In addition, LISA uses the excellent IPython notebook framework and a set of notebooks are provided for live experiments on a target platform.
This project is licensed under Apache-2.0.
This project includes some third-party code under other open source licenses. For more information, see lisa/tools/LICENSE.*
Contributions are accepted under Apache-2.0. Only submit contributions where you have authored all of the code. If you do this on work time make sure your employer is cool with this.