Map Memory Analysis

GCC map file memory analysis and report. It's helpful for locating the large costs, optimizing and debugging.

Workflow

1. Read sections from map file with scanner, a async-generator.
2. Classify section contents into certain modules dictionary.
3. Format module into DataFrame.
4. Render the DataFrame into html page.

Required

The following python third-party libraries need to be installed first, e.g. pip install lib_name:

• pandas: A powerful data processor.
• pyecharts: A expressive chart library.
• pyyaml: YAML is the data serialization language of the configuration file of this project.

Start working

Configuration:

The following configuration file is used for a more accurate analysis.

# Classify the objects of section into certain classification.
# Three rule types are provided:
# 1.[match_re. The re needs to have one group "()"],
# 2.[match_re, classification],
# 3.[match_str, classification]
classification_rules:
  - match_re: ^\./sdk/(\w+)/[/\w]*\w+\.o
  - match_re: ^\.[/\w\-]+\.o
    classification: application
  - match_str: lib/gcc/arm-none-eabi
    classification: gcc

# Group sections according to your lds file and count the group size.
section_group_sizes:
  text: [ sections... ]
  data: [ sections... ]

Generate:

Run the test case from command line

cd map-memory-analysis
python test/test.py

or add the following code to your own python project

from mapreport import Report

Report(map_file_path, config_file_path, output_path).generate_report()

the report will be generated to output_path/report.

Report:

This is a demo report Report.html.