GitLab and GitLab CI/CD
What is GitLab?
GitLab is an integrated DevOps platform that combines:
- Git repository hosting
- Source code management
- Continuous Integration / Continuous Deployment (CI/CD)
- Issue tracking
- Code review
- Project management
- Package and container registry
Unlike traditional setups that require multiple tools such as Jenkins, Jira, and separate Git servers, GitLab provides an all-in-one solution.
What is GitLab CI/CD?
GitLab CI/CD is the built-in automation system inside GitLab.
It automatically performs tasks such as:
- Building projects
- Running unit tests
- Running regression test
- Running benchmark tests
- Packaging software
- Deploying applications
- Generating reports
whenever developers push code or create merge requests.
Core Concepts
1. Pipeline
A pipeline is an automated workflow.
Typical pipeline flow:
Code Commit
↓
Build
↓
Test
↓
Benchmark
↓
Package
↓
Deploy
2. .gitlab-ci.yml
GitLab CI/CD pipelines are defined using a YAML configuration file stored directly in the repository.
Example:
stages:
– build
– test
build_job:
stage: build
script:
– cmake .
– make
test_job:
stage: test
script:
– ctest
This approach makes CI/CD pipelines version-controlled and reproducible.
3. GitLab Runner
A Runner is the machine or environment that executes CI/CD jobs.
GitLab Runners can run on:
- Linux
- Windows
- macOS
- Docker containers
- Kubernetes clusters
This enables scalable and distributed automation.
Main Advantages of GitLab CI/CD
1. All-in-One Platform
GitLab integrates:
| Feature | Included |
| Git Repository | Yes |
| CI/CD | Yes |
| Issue Tracking | Yes |
| Merge Requests | Yes |
| Wiki | Yes |
| Artifact Storage | Yes |
| Container Registry | Yes |
This reduces the need for multiple external systems.
2. Better Integration with Development Workflow
GitLab CI/CD is tightly integrated with Merge Requests.
Example workflow:
Developer creates Merge Request
↓
Pipeline automatically starts
↓
Build and tests run
↓
Benchmark executes
↓
Results displayed in Merge Request
This improves code quality and team collaboration.
3. Version-Controlled CI/CD
All pipeline configurations are stored in Git.
Benefits:
- Easy to track changes
- Easier collaboration
- Better reproducibility
- Simplified maintenance
This is considered a major advantage over older CI systems like Jenkins.
4. Strong Docker and Container Support
GitLab CI/CD has native Docker support.
Example:
image: ubuntu:24.04
build:
script:
– apt update
– apt install -y cmake
Each pipeline can run inside a clean and isolated container environment.
5. Excellent for Automated Testing and Benchmarking
GitLab CI/CD is widely used for:
- Unit testing
- Integration testing
- Regression testing
- Performance benchmarking
- Nightly builds
Especially in:
- C++ projects
- Game engines
- CAD software
- AI infrastructure
- Multi-platform systems
Typical Workflow
Step 1 — Developer Pushes Code
git push
Step 2 — Pipeline Starts Automatically
GitLab detects changes and triggers the pipeline.
Step 3 — Runner Executes Jobs
Example tasks:
- Compile source code
- Run tests
- Execute benchmarks
- Generate artifacts
Step 4 — Results Are Reported
GitLab displays:
- Build status
- Test reports
- Benchmark logs
- Artifacts
- Failure details
directly in the web interface.
Artifacts
GitLab CI/CD can store generated files such as:
- Executables
- Benchmark JSON reports
- Test logs
- Screenshots
- Build packages
Example:
artifacts:
paths:
– benchmark.json
Artifacts can later be downloaded or used in later pipeline stages.
Comparison with Jenkins
| Feature | GitLab CI/CD | Jenkins |
| Integrated with Git platform | Yes | No |
| Configuration | YAML | UI + Groovy |
| Plugin dependency | Low | High |
| Docker support | Native | Plugin-based |
| Pipeline versioning | Excellent | Limited |
| Maintenance complexity | Lower | Higher |
Why Many Teams Prefer GitLab CI/CD Today
Modern development teams increasingly prefer GitLab CI/CD because it:
- simplifies DevOps infrastructure,
- reduces maintenance burden,
- improves automation consistency,
- integrates naturally with Git workflows,
- scales well for modern software projects.
It is especially popular among engineering-focused teams working on:
- C++
- graphics engines
- CAD systems
- cloud-native applications
- AI platforms
- large-scale automated testing systems
Example Modern Development Stack
A common modern setup is:
GitLab CE
+
GitLab Runner
+
Docker
+
CTest / Google Benchmark
+
Grafana
+
InfluxDB
This architecture supports:
- automated builds,
- nightly benchmark testing,
- regression detection,
- performance monitoring,
- multi-platform CI/CD workflows.