Exploring Multicore Analysis for Simulation

When it comes to speeding up model analysis, Multicore Analysis (MCA) is a game-changer. By distributing your model workload across multiple instances of ExtendSim, you can significantly enhance performance and efficiency. Here’s how you can make the most of it.

Why Use Multicore Analysis? 

Think of MCA as having multiple hands working on a task simultaneously. Instead of running a model or models sequentially to test different scenarios, you can run multiple instances of the model/s in parallel. This not only saves time, but also allows for more comprehensive analysis. 

Running Copies of a Model in Parallel 

Instead of sequentially running one model to test various scenarios, use MCA to spawn Child-Nodes (new instances) of ExtendSim. These Child-Nodes can perform parallel model execution, drastically reducing the time needed for analysis. 

Open Additional Instances for Development 

The Multicore Analysis feature also allows you to manually launch multiple instances of ExtendSim, enabling the concurrent running of multiple models. Manually opening additional instances of ExtendSim can simplify model debugging. You can run different models on separate cores, making it easier to identify and fix issues without slowing down your workflow. 

Benefits of Multicore Analysis

• Speed: Running models in parallel significantly cuts down the time required for analysis.
• Efficiency: It allows for handling larger and more complex simulations that would be impractical to run sequentially.
• Scalability: As your simulation needs grow, the ability to run models concurrently on more than the default 4 instances of ExtendSim is available. For details and pricing, visit ExtendSim.com. 

Conclusion 

Multicore Analysis is a powerful tool for anyone looking to enhance their simulation capabilities. By distributing workloads across multiple instances of ExtendSim, you can achieve faster, more efficient, and scalable model analysis. Whether you’re running multiple instances for parallel execution or simplifying debugging, MCA can make a significant difference in your workflow.

What are Response Definitions and why are they so critical?

When setting up a simulation, defining responses is crucial. These responses are the model outputs you select to automatically collect results from each replication. Let’s dive into the different types of responses you can set up in the ExtendSim Analysis Manager block and how it will help you streamline your analytical processes so you can grab the simulation results you need.

Understanding Response Definitions in Simulations 

Responses are essentially the data points you want to track during your simulation. By defining these, you ensure that the results you need are collected automatically, saving you time and effort. Think of them as the specific results or data points you want to track and analyze after each run. Here’s a bit more detail:

1. What They Are: Response definitions specify what you’re looking to measure in your simulation. This could be anything from performance metrics, error rates, throughput, or any other relevant data points that are crucial for your analysis.
2. How They Work: When you set up your simulation, you define these responses in the Analysis Manager block. For example, if you’re simulating a manufacturing process, your responses might include the number of units produced, the time taken for each unit, or the defect rate.
3. Data Collection: At the end of each simulation run, the Analysis Manager block automatically collects the data based on these response definitions. It then stores this data in the Analysis database for you to review and analyze later.
4. Why They Matter: Having clear response definitions helps ensure that you’re capturing all the necessary data to evaluate the performance and outcomes of your simulation accurately. It makes your analysis more structured and meaningful. 

Types of Responses You Can Define

Remember from last week’s article, Introducing the New Analysis Manager Block the Analysis Manager acts as a data management system for consolidated control of parameters and collection of model results. It automatically creates an Analysis database and stores all your core analytical process definitions for you – both factor and response definitions - plus it collects and catalogues results from your replications for superb record-keeping and further analysis. The Analysis Manager can collect:

• Block Responses that can be added to the Analysis Manager using either the:
• Right-click Method: Simply right-click on any output parameter or checkbox in any block dialog or on a cloned output in your model and choose Add Response.
• Search Model Method: Click the Search Model button to open the dialog of the Search Blocks block (found in the Utilities library). This allows you to build a filtered list of blocks and their associated dialogs to add as responses to your model.
• Database Responses are added by clicking the green +/- button in the lower right corner of the DB factors table and selecting Add DB response(s) to open the Database Address Selector. From here, you can choose a field or record to use as a response. 
• Reliability Responses - If your model includes one or Reliability Block Diagrams (RBDs), the Responses tab of the Analysis Manager will display a table for adding Reliability Responses. Add them by using the:
• Edit in DB Button: Opens the Reliability Responses table for direct editing.
• Use Model Data Button: Fills the Reliability Responses table with all the fail-modes currently defined in the model. 

So, in a nutshell, response definitions are your way of telling the Analysis Manager exactly what results you’re interested in. This ensures that all the important data is collected and stored in the Analysis database systematically, making your analysis process much smoother and more efficient.