Procesador multinúcleo objetivo

Modelice la ejecución simultánea de sistemas diseñados para su despliegue en un sistema multinúcleo o FPGA

La programación multinúcleo, o el modelado de la ejecución simultánea, ayuda a crear sistemas simultáneos para su despliegue en sistemas de procesadores multinúcleo y multiprocesadores. Algunos ejemplos de estos sistemas son los de procesamiento de señales y los de control de plantas. Las técnicas de partición y aplicación de Simulink^® ayudan a superar los retos habituales en el diseño de sistemas para la ejecución simultánea.

La figura muestra un sistema de ejemplo con múltiples funciones diseñadas para ejecutarse en un sistema multiprocesador basado en CPU y FPGA. El sistema se divide en varios componentes que se asignan al planificador de tareas de la CPU o a la FPGA.

Para aprender los conceptos básicos de programación multinúcleo, consulte Concepts in Multicore Programming. Para obtener información sobre cómo diseñar sistemas para la ejecución simultánea en Simulink, consulte Multicore Programming with Simulink.

Funciones

`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

Clases

Simulink.GlobalDataTransfer Configure concurrent execution data transfers

Ejemplos y procedimientos

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.
Optimize and Deploy on a Multicore Target
Configure a model for concurrent execution using explicit partitioning and deploy it to a target.
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
Learn how to implement task parallelism for a system in a Simulink model.
Implement Pipelining in Simulink
This example shows how to implement pipelining for a system in a Simulink model.
Assign Tasks to Cores for Multicore Programming
This example shows how to take advantage of executing code on a multicore processor by graphical partitioning.
Implement an FFT on a Multicore Processor and an FPGA
This example shows you how to take advantage of a multicore processor target with FPGA acceleration by graphically partitioning a model.
Multicore Deployment of a Plant Model
This example illustrates how to take advantage of executing multithreaded code on a multicore processor using graphical partitioning.

Conceptos

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.
Concurrent Execution Window: Main Pane
Parameters for configuring tasks for concurrent execution.
Data Transfer Options for Concurrent Execution
This tab displays the data transfer options for configuring models for targets with multicore processors.
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.

Información relacionada

Programación multinúcleo con Simulink

Ejemplos destacados

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.

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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.

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Multicore Programming of a Field-Oriented Control on Zynq

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

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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.

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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.

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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.

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Tiled Processing of 2D Signals with For Each Subsystem

Use the For Each Subsystem. In this example the operations are performed on matrices.

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Multithreaded Simulation Using For Each Subsystem

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

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