Manage model complexity as systems grow with workflows for model architecture, data management, collaboration, testing, verification, and simulation performance. Develop and maintain large-scale Simulink models more effectively across teams.
Best Practices for Scaling Up
Learn from MathWorks experts in the video series, Simulink Best Practices for Large-Scale Modeling, which covers approaches for developing and managing large-scale Simulink models collaboratively. The five-part series covers practical strategies for components and interfaces, collaborative development, data management, and speeding up simulation.
Speed Up Simulations
Improve simulation throughput using Accelerator Mode, Fast Restart, cache files, performance analysis tools, and parallel computing techniques.
Troubleshoot and Speed Up Simulation Performance
Identify simulation slowdown causes and explore effective solutions to help accelerate your simulations.
Simulink Cache
Get simulation results faster by using shared model artifacts.
Parallel Simulations
Run multiple independent simulations on a multicore computer or computer cluster.
Performance Advisor
Analyze your model for common simulation performance bottlenecks.
Fast Restart
Run consecutive simulations more quickly.
Choosing a Simulation Mode
Select the most efficient mode for the workflow you are performing.
Just-in-Time Acceleration Builds
Quickly build the top-level model for improved performance when running simulations in accelerator mode.
Success Stories
Industry leaders are using Simulink to design and simulate large and complex systems. Learn how they are using these models to accelerate their development and test new ideas.
Scale Your Models
Explore documentation that helps you make key decisions about model architecture and management. These topics provide details and guidelines on when to use certain modeling patterns for common situations, including when scaling up models.
Model Composition
Build scalable architectures in Simulink using Model References, reusable components, subsystem references, and interface design best practices.
Data Management
Organize parameters, signals, data types, and configuration information using Simulink Data Dictionaries and scalable data architectures.
File Management
Manage project files, dependencies, and workspace configuration using MATLAB Projects and project references.
Source Control and Continuous Integration
Integrate Simulink models with Git™, automated testing workflows, version control systems, and CI/CD pipelines.
Verification and Validation
Improve model quality through testing, coverage analysis, static analysis, and component verification workflows.
External Component Integration
Integrate C, C++, Python®, Functional Mockup Units (FMUs), and other external components into Simulink models.
Expand Your Knowledge with Training
Expand and solidify your knowledge of best practices for scaling up with the Simulink Model Management and Architecture course or browse other courses in our catalog.
Large-Scale Simulink Modeling FAQs
Large-scale Simulink projects are easier to maintain when models are componentized using Model References, interfaces are simplified with buses, data is managed with Simulink Data Dictionaries, and development is organized using MATLAB Projects.
Model References support modular development, parallel engineering workflows, component reuse, independent testing, and faster builds. They improve simulation performance through incremental workflows, cache reuse, selective acceleration, independent compilation of model components, and support for Accelerator Mode and Rapid Accelerator Mode.
Libraries are typically used for small, reusable utilities. Subsystem References are useful for shared components and certain Simscape or HDL workflows. Model References are recommended for large-scale componentization, reusable architectures, independent simulation, and simulation acceleration.
Buses reduce interface complexity by grouping related signals into a single connection. This improves model readability, maintainability, and scalability while reducing diagram clutter.
Simulink Data Dictionaries are dedicated .sldd files used to store parameters, signals, bus objects, enumerations, value types, and configuration sets outside the model. They provide a scalable alternative to storing all data in the MATLAB base workspace.
The base workspace is useful for prototyping and simple projects. For large projects, distributed teams, and long-term maintenance, Simulink Data Dictionaries are generally recommended because they provide better organization, persistence, change tracking, and data partitioning.
Yes. Data Dictionaries can reference other Data Dictionaries, allowing common parameters, bus definitions, enumerations, and configuration data to be shared across multiple models and model hierarchies.
MATLAB Projects simplify environment setup, path management, dependency tracking, source control integration, project references, testing workflows, and collaboration across engineering teams.
MATLAB Projects includes a Dependency Analyzer that visualizes dependencies between models, libraries, data dictionaries, test files, and referenced projects to help teams understand the impact of changes.
MATLAB provides graphical compare and merge tools that highlight functional and non-functional changes in Simulink models, Data Dictionaries, and other supported files.
Common techniques include using Simulink Profiler, Solver Profiler, Performance Advisor, Accelerator Mode, Rapid Accelerator Mode, Fast Restart, model componentization, cache files, and parallel simulation workflows.
Fast Restart allows repeated simulations without recompiling the model when only parameter values change. This can significantly reduce initialization time during parameter tuning and design exploration. Accelerator Mode generates compiled code for model execution, reducing simulation overhead and improving simulation performance compared to Normal Mode. Rapid Accelerator Mode creates a standalone executable simulation target, offering even greater simulation speed improvements for many workflows.
Simulink Cache Files store compiled artifacts and simulation targets so they can be reused between sessions. This reduces rebuild times and speeds model initialization.
The parsim workflow enables multiple Simulink simulations to run concurrently across available CPU cores, reducing the total time required for parameter sweeps, Monte Carlo studies, and design optimization.
Componentization improves readability, reusability, collaboration, traceability, testing efficiency, simulation performance, and long-term maintainability. It is a foundational practice for scaling Model-Based Design environments.
