hathatehack / map-memory-analysis

GCC map file memory analysis and report.

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.