Multicore Processor Targets

Model concurrent execution for systems designed for deployment on a multicore or FPGA system

Multicore programming, or modeling for concurrent execution, helps you to create concurrent systems for deployment on multicore processor and multiprocessor systems. Examples of such systems are signal-processing and plant-control systems. Simulink^® partitioning and mapping techniques help you to overcome common challenges in designing systems for concurrent execution.

The figure shows a sample system with multiple functions designed to execute on a CPU- and FPGA-based multiprocessor system. The system is partitioned into multiple components that are mapped to the CPU task scheduler or the FPGA.

To learn the fundamentals of multicore programming, see Concepts in Multicore Programming. For information on how to design systems for concurrent execution in Simulink, see Multicore Programming with Simulink.

Functions

`Simulink.architecture.config`	Create or convert configuration for concurrent execution
`Simulink.architecture.add`	Add tasks or triggers to selected architecture of model
`Simulink.architecture.delete`	Delete triggers and tasks from selected architecture of model
`Simulink.architecture.find_system`	Find objects under architecture object
`Simulink.architecture.get_param`	Get configuration parameters of architecture objects
`Simulink.architecture.importAndSelect`	Import and select target architecture for concurrent execution environment for model
`Simulink.architecture.profile`	Generate profile report for model configured for concurrent execution
`Simulink.architecture.register`	Add custom target architecture to concurrent execution target architecture selector
`Simulink.architecture.set_param`	Set architecture object properties

Objects

Simulink.GlobalDataTransfer Configure concurrent execution data transfers

Tools

Concurrent Execution Tool

Specify tasks, triggers, and nodes for concurrent execution of Simulink model

Topics

Understand Multicore Programming in Simulink

Concepts in Multicore Programming
Theory relevant to modeling for concurrent execution.
Multicore Programming with Simulink
Modeling for concurrent execution using Simulink.
Implicit and Explicit Partitioning of Models
Learn about the key differences between implicit and explicit partitioning.
Supported Targets for Multicore Programming
Deploy concurrent execution models to supported multicore targets.
Limitations with Multicore Programming in Simulink
Limitations and considerations when partitioning a model for concurrent execution.

Configure Your Model

Configure Your Model for Concurrent Execution
Learn how to configure your Simulink model to take advantage of concurrent execution.
Specify a Target Architecture
Choose or define a target architecture for a model configured for concurrent execution.
Partition Your Model Using Explicit Partitioning
Add tasks, create partitions, and map individual tasks to partitions using explicit partitioning.
Configure Data Transfer Settings Between Concurrent Tasks
Specify options for handling data transfers between concurrently executing partitions.

Implement Parallelism in Simulink

Implement Data Parallelism in Simulink
This example shows how to implement data parallelism for a system in a Simulink model.
Implement Task Parallelism in Simulink
This example shows how to implement task parallelism for a system represented in a Simulink® model.
Implement Pipelining in Simulink
This example shows how to implement pipelining for a system represented in a Simulink® model.

Code Generation

Optimize and Deploy on a Multicore Target
Configure a model for concurrent execution using explicit partitioning and deploy it to a target.
Assign Tasks to Cores for Multicore Programming
This example shows how to take advantage of executing code on a multicore processor by graphical partitioning.

Related Information

Multicore Programming with Simulink

Featured Examples

Assign Tasks to Cores for Multicore Programming

Take advantage of executing code on a multicore processor by graphical partitioning. This example requires Simulink® Coder™ software to generate multithreaded code.

Open Example

Implement an FFT on a Multicore Processor and an FPGA

Take advantage of a multicore processor target with FPGA acceleration by graphically partitioning a model. This example requires Simulink Coder™ to generate multi-threaded code and HDL Coder™ to generate HDL code. You cannot generate HDL code on Macintosh systems.

Open Model

Multicore Programming of a Field-Oriented Control on Zynq

Demonstrates how to implement a control algorithm that contains multiple rates on Zynq®. To take advantage of both the cores and the FPGA hardware, the example uses a graphical partitioning approach such that code from different partitions is distributed across the cores and the hardware.

Open Model

Multicore Deployment of a Plant Model

Illustrates how to take advantage of executing multithreaded code on a multicore processor using graphical partitioning. This example requires Simulink® Coder™ to generate multithreaded code.

Open Model

Modeling Objects with Identical Dynamics Using For Each Subsystem

Model multiple objects with identical dynamics using the For Each subsystem. The number of objects is parameterized by the length of the input signal.

Open Model

Vectorizing a Scalar Algorithm with a For-Each Subsystem

Use the for-each subsystem. In this example, the operations are performed on a vector for simplicity.

Open Model

Tiled Processing of 2-D Signals with For-Each Subsystem

Use a for-each subsystem. In this example, the operations are performed on matrices.

Open Model

Multithreaded Simulation Using For Each Subsystem

Speed up execution of a model on multiple cores using a For Each Subsystem in Rapid Accelerator simulation.

Open Model

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