Main Content

Multicore Simulation and Code Generation of Dataflow Domains

Simulation of Dataflow Domains

Simulation of dataflow domains leverages the multicore CPU architecture of the host computer. It automatically partitions your model and simulates the subsystem using multiple threads.

The first time you simulate a dataflow domain, the simulation is single threaded. During this simulation, the software performs a cost-analysis. The next time the model compiles, the software automatically partitions the system for multithreaded execution. Subsequent simulations are multithreaded.

Code Generation of Dataflow Domains

Dataflow domains support code generation for both single-core and multi-core targets. When all blocks inside a dataflow subsystem support multithreading, and the model is configured for multicore code generation, the generated code is multithreaded. During code generation, the dataflow subsystem is automatically partitioned according to the specified target hardware.

Types of Parallelism

In both simulation and code generation of models with dataflow domains, the software identifies possible concurrencies in your system, and partitions the dataflow domain using the following types of parallelism.

Task Parallelism

Task parallelism achieves parallelism by splitting up an application into multiple tasks. Task parallelism involves distributing tasks within an application across multiple processing nodes. Some tasks can have data dependency on others, so all tasks do not run at exactly the same time.

Consider a system that involves four functions. Functions F2a() and F2b() are in parallel, that is, they can run simultaneously. In task parallelism, you can divide your computation into two tasks. Function F2b() runs on a separate processing node after it gets data Out1 from Task 1, and it outputs back to F3() in Task 1.

The figure shows the timing diagram for this parallelism. Task 2 does not run until it gets data Out1 from Task 1. Hence, these tasks do not run completely in parallel. The time taken per processor cycle, known as cycle time, is

t = tF1 + max(tF2a, tF2b) + tF3.

Model Pipeline Execution (Pipelining)

The software uses model pipeline execution, or pipelining, to work around the problem of task parallelism where threads do not run completely in parallel. This approach involves modifying the system to introduce delays between tasks where there is a data dependency.

In this figure, the system is divided into three tasks to run on three different processing nodes, with delays introduced between functions. At each time step, each task takes in the value from the previous time step by way of the delay.

Each task can start processing at the same time, as this timing diagram shows. These tasks are truly parallel and they are no longer serially dependent on each other in one processor cycle. The cycle time does not have any additions but is the maximum processing time of all the tasks.

t = max(Task1, Task2, Task3) = max(tF1, tF2a, tF2b, tF3).

Pipelining can be used when you can introduce delays artificially in your concurrently executing system. The resulting overhead due to this introduction must not exceed the time saved by pipelining.


When the cost analysis identifies a single block in a system that is computationally dominant, the system uses unfolding technology. Unfolding is a technique to improve throughput through parallelization. The software duplicates the functionality of the computationally intensive block, divides the input data into multiple pieces, and the processor performs the same operation on each piece of data.

Unfolding is used in scenarios where it is possible to process each piece of input data independently without affecting the output, and the block is stateless or contains a finite number of states.

Improve Simulation Throughput with Multicore Simulation

Simulation of dataflow domains leverages the multicore CPU architecture of the host computer. It automatically partitions your model and simulates the subsystem using multiple threads. To increase the throughput of your system you can use latency or you can use the settings provided when you perform multicore analysis. For more information about improving simulation performance, see Perform Multicore Analysis for Dataflow.

Generate Multicore Code from a Dataflow Subsystem

Configure Your Model for Multicore Code Generation

Code generation requires a Simulink® Coder™ or an Embedded Coder® license. Single-core and multicore targets are supported.

Code generated for single-core targets generates nonvirtual subsystem code.

To generate multicore code, you must configure your model for concurrent execution. If you do not configure your model for concurrent execution, the generated code will be single threaded.

  1. In Configuration Parameters > Solver > Solver selection, choose Fixed-step for the Type and auto (Automatic solver selection) for the Solver.

  2. Select the Allow tasks to execute concurrently on target check box in the Solver pane under Solver details. Selecting this check box is optional for models referenced in the model hierarchy. When you select this option for a referenced model, Simulink allows each rate in the referenced model to execute as an independent concurrent task on the target processor.

  3. In Configuration Parameters > Code Generation > Interface > Advanced parameters, clear the MAT-file logging check box.

  4. Click Apply to apply the settings to the model.

Generate Multicore Code

To generate multicore code, the software performs cost analysis and partitions the dataflow domain based on your specified target. The partitioning of the dataflow domain may or may not match the partitioning during simulation.

The generated C code contains one void(void) function for each task or thread created by the dataflow subsystem. Each of these functions consists of:

  • The C code corresponding to the blocks that were partitioned into that thread

  • The code that is generated to handle how data is transferred between the threads.

    This can be in the form of pipeline delays or target-specific implementation of data synchronization semaphores.

The following multicore targets are supported for code generation.

  • Linux®, Windows®, and Mac OS desktop targets using ert.tlc and grt.tlc.

  • Simulink Real-Time™ using slrealtime.tlc.

  • Embedded Coder targets using Linux and VxWorks® operating systems.

Code generated for grt.tlc and ert.tlc desktop targets is multithreaded using OpenMP within the dataflow subsystem. Code generated for Embedded Coder targets is multithreaded using POSIX threads.

For multicore custom targets, use the DataflowThreadingImplementation parameter to select POSIX threads (Pthreads) or OpenMP threading implementation for multicore custom targets using dataflow domain. The parameter takes the values off, OpenMP, or Pthreads. By default, the parameter is set to Off which means that dataflow generates single core code (not multiple threads).

set_param(myModel, 'DataflowThreadingImplementation','off') 
For information about customizing system target files, see Customize System Target Files (Embedded Coder).

If your system contains blocks that do not support multithreaded execution, the generated code is single-threaded.

To build the model and generate code, press Ctrl+B.

In the generated code, you can observe calls to the threading API and the pipeline delays that were inserted into the model to create more concurrency.

The following example shows that there are two thread functions generated by dataflow subsystem, ex_staple_counting_ThreadFcn0 and ex_staple_counting_ThreadFcn1, which are executed using OpenMP sections. These functions are part of the dataflow_subsystem_output/step() function.

static void ex_staple_counting_ThreadFcn0(void)

	  if (pipeStage_Concurrent0 >= 2) {
	    /* Delay: '<S3>/TmpDelayBufferAtDraw Markers1Inport1' */
	           &ex_staple_counting_DW.TmpDelayBufferAtDrawMarkers1I_i[0], 202176U *
	    /* Delay: '<S3>/TmpDelayBufferAtDraw Markers1Inport2' */
	    line_idx_1 = (int32_T)ex_staple_counting_DW.CircBufIdx * 100;
	           100U * sizeof(real_T));
void ex_staple_counting_Concurrent0(void)
	#pragma omp parallel num_threads(3)
	#pragma omp sections
	#pragma omp section
	#pragma omp section
	#pragma omp section

See Also

Related Topics