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coder.ExternalDependency can give a Simulink model a shared MATLAB-facing interface to external C or C++ code, while letting each generated build supply its own target-specific libraries and settings. That can avoid packaging an S-function when the real need is simply to call external code. It does not make a Linux library usable on QNX, or establish that a particular QNX toolchain is supported: Linux and QNX remain separate target builds whose compiler, ABI, architecture, sysroot, and dependencies must be verified for the intended MATLAB release and QNX SDP.
What does coder.ExternalDependency do?
coder.ExternalDependency is an abstract base class for connecting MATLAB code intended for code generation to external code. A subclass can encapsulate C or C++ source, object files, or libraries, keeping the MATLAB-facing interface separate from implementation and build details. MathWorks describes it this way: “You can develop an interface to external code by using the base class coder.ExternalDependency.”
The wrapper has two responsibilities: define when the dependency is available and tell the generated build how to find and link it. The documented subclass contract includes:
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minutegetDescriptiveName, to identify the dependency.isSupportedContext(buildContext), to determine whether it is available in the current build context. Use this to reject unsupported targets with a clear error instead of assuming a library exists everywhere.updateBuildInfo, to add the required source files, include paths, libraries, or linker options to the build.
The methods that invoke the external function are compiled and can call it with coder.ceval. If the same wrapper also needs to run interactively in MATLAB, branch on coder.target('MATLAB') so MATLAB execution uses suitable MATLAB behavior while generated code calls the external implementation.
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How can one model serve Linux and QNX?
Keep shared algorithm behavior in one model, but treat Linux and QNX as separate build targets with separate artifacts. MathWorks states that generated binaries are functional for the host hardware and operating system by default. Building for another platform requires a suitable hardware support package with target configuration or a registered custom toolchain; another documented route is to generate source and build it manually where the target build system is already configured.
For each target, updateBuildInfo can provide the applicable library names and extensions, include directories, target-specific source files, and linker options. MathWorks documents build-context platform information, including getStdLibInfo, for handling platform-specific library extensions. The target build still has to use libraries and settings compatible with that platform.
A shared wrapper is an interface abstraction, not a binary portability layer. Before expecting a target build to link, verify the target architecture, ABI, compiler, sysroot, dependency versions, and linker and runtime behavior. A Linux .a or .so library is not thereby a QNX-compatible library.
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The MathWorks deployment guidance covers Linux workflows and general target-toolchain and manual-build routes. It does not establish a currently supported pairing of QNX SDP release, compiler, processor architecture, and sysroot, or confirm that a QNX-specific support package is available for the release you use. Check those details against the exact MATLAB/Simulink release and QNX SDP configuration before treating QNX as a configured target.
- Confirm that your intended target compiler and sysroot are supported by, or can be registered with, the chosen build workflow.
- Confirm that each external dependency has a QNX-compatible build for the target architecture and ABI.
- Confirm that the generated build receives the QNX-specific headers, sources, libraries, and linker settings.
- Build and verify on the actual target or a suitable target environment; a successful host build does not establish QNX compatibility.
When is this preferable to an S-function?
Use coder.ExternalDependency when the natural interface is a MATLAB/Coder-facing wrapper around external C or C++ calls, and the goal is to control how that dependency enters generated builds. It can make target-specific build choices explicit without requiring the external interface itself to be packaged as a Simulink S-function block.
An S-function remains appropriate when the dependency is naturally a Simulink block or relies on its simulation integration, scheduling behavior, or established block-based build mechanism. MathWorks supports S-function dependency mechanisms such as SFunctionModules and rtwmakecfg.m; the choice is about the right interface and deployment workflow, not a universal limitation of S-functions.
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There are additional handoff considerations when using an S-function for downstream code generation. The S-function target emits code conforming to the Simulink C MEX S-function API. Code-generation deployment can require more than the MEX binary: generated C or C++ source, a header, a platform-dependent MEX file, and the _sfcn_rtw folder. The generated S-function’s Hardware Implementation settings correspond to the host where it was built and must match the receiving model for code generation. These requirements can add files and host-configuration coordination to a reusable component handoff.
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Choose the mechanism according to where the external code enters the model:
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- Model or system-target level: use Configuration Parameters > Code Generation > Custom Code for additional source files, libraries, and include folders. TLC hooks are another option.
- Block-based dependency: use the relevant S-function or blockset mechanisms, including header paths, makefile rules,
SFunctionModules, orrtwmakecfg.m. - External-code wrapper: implement
updateBuildInfoso the generated build receives the files and options required for that target.
Inspect the generated build information and makefiles to see which headers, sources, libraries, runtime support, shared utilities, and compiler or linker requirements are actually included. For relocation, package only the required generated artifacts; MathWorks recommends packNGo rather than copying an entire code-generation folder indiscriminately.
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What is a practical deployment sequence?
- Define the shared interface. Keep the model-facing API and shared algorithm behavior in the model or wrapper; keep target-specific implementation and linkage details in the external dependency configuration.
- Implement and check the wrapper. Provide the documented subclass methods, use
isSupportedContextto reject unsupported build contexts, and usecoder.target('MATLAB')where interactive MATLAB execution needs a different path from generated code. - Configure each target independently. Supply the correct headers, compatible libraries or sources, and linker options for Linux and for the specific QNX target configuration. Do not reuse a host binary merely because the model is shared.
- Generate and inspect code for each target. Review the generated build information and makefiles to confirm the intended dependencies and toolchain settings are present.
- Build, link, and verify the component. For component deployment, the target environment and an external
mainprogram integrate and schedule the generated component code. MathWorks describes building a component library or source and linking it with the externalmainand target code; use generated-code verification workflows before deployment. - Package the handoff. Include the required generated artifacts and dependency information rather than copying the whole code-generation directory by default.
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