This article outlines the process to measure the maximum memory usage of a GCC compile process running within a Bookworm Docker image. To accomplish this, we'll leverage the /usr/bin/time utility, which is commonly available in Linux distributions and enables comprehensive monitoring of various resources, including maximum resident set size (max RSS).
Bookworm Docker Image
The Bookworm Docker image is derived from the official GCC Docker image and typically contains the GCC compiler, associated libraries, and other essential development tools. You can pull the latest Bookworm image using the following command:
docker pull bookworm/base:latest
Memory Usage Measurement using /usr/bin/time
The /usr/bin/time command is typically installed alongside common Linux utilities such as gcc. This command permits the evaluation of various resource consumption metrics such as CPU time, wall clock time, and maximum resident set size (max RSS)—representing the peak memory usage of a process during its lifetime.
Invoking /usr/bin/time
You can use the /usr/bin/time command as follows to compile your C/C++ code and concurrently obtain memory usage statistics:
/usr/bin/time -v gcc -o output_file source_file.c
Interpreting Output
After executing the aforementioned command, the resulting output will feature the maximum resident set size (max RSS) metric:
...
Command being timed: "gcc -o output_file source_file.c"
User time (seconds): 0.12
System time (seconds): 0.04
Percent of CPU this job got: 109%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:00:01
Average shared text size (kbytes): 0
Average unshared data size (kbytes): 8
Average stack size (kbytes): 8
Average total size (kbytes): 16
Maximum resident set size (kbytes): 14904
...
The Maximum resident set size (kbytes) indicates the peak memory consumption during the entire process, which represents the crucial memory measurement we strive to capture.
Summarizing Memory Usage Patterns
To discern the overall memory usage patterns of a Dockerized GCC build system, you may opt to execute a series of test compilations, each with an increasing compilation complexity (e.g., source code file size, preprocessor directives, compile-time options). Leveraging this approach, you can subsequently derive the correlation between the compilation intricacy and the resulting memory consumption.
Automating Memory Usage Assessment
You can utilize a simple shell script to streamline the execution of test compilations and facilitate the acquisition of the /usr/bin/time output. For instance:
#!/bin/bash
# List of source code files
files=("source_file1.c" "source_file2.c" ...)
for file in "${files[@]}"; do
output_file="output_${file%.c}"
echo "> Timing: gcc -o ${output_file} ${file}"
/usr/bin/time -v gcc -o ${output_file} ${file} &>> time_stats.txt
done
As demonstrated, measuring the maximum memory usage for a GCC compile process executed within a Dockerized Bookworm environment essentially requires orchestrating the execution of the /usr/bin/time utility and analyzing the resulting output.
- Bookworm Docker image: https://hub.docker.com/r/bookworm/base
/usr/bin/timeutility: https://man7.org/linux/man-pages/man1/time.1.html