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How much code will you need to change?
Start by locating where your application meets the GPU platform. A workload that relies mainly on a supported framework may need fewer source changes than one built around low-level GPU interfaces. That is a useful expectation, not a guarantee: framework compatibility, custom operators, library dependencies, and deployment requirements can all add migration work.
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CUDA and HIP are separate platforms; their tools and APIs are not directly interchangeable. Translation tools can help with supported patterns, but they do not establish that the whole application will compile, behave correctly, or perform well on the destination.
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- Mostly framework-level code: Check that the destination supports the GPU, operating system, driver, accelerator stack, and framework versions you need. Then test the actual workload, including any custom operators or extensions.
- Direct CUDA use: Expect to audit and adapt runtime or driver calls, libraries, kernels, build configuration, and packaging. The more platform-specific the code, the less likely a simple source translation will be sufficient.
What should you check before choosing a destination?
Framework, GPU, operating system, and software-stack compatibility
Verify the exact combination of GPU, operating system, driver, accelerator software release, and framework version for the target environment. Support for a framework in general does not mean every release or system configuration is supported. AMD lists PyTorch, TensorFlow, and JAX among the frameworks supported by ROCm, while directing users to check support for their specific release and system configuration.
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Use the destination vendor’s current compatibility matrix to select versions; do not infer a supported combination from an older guide or a general product page. Confirm that any required model-serving framework, framework extension, and custom operator are covered as well.
Runtime and driver interfaces
Audit direct runtime and driver API calls rather than assuming that changing names will port the application. AMD’s HIP migration guidance describes differences in API organization, context behavior, memory copies, and module management between CUDA and HIP. Code that depends on low-level driver features may need a deliberate redesign or manual adaptation.
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Libraries and extensions
Inventory every dependency that participates in GPU work: math and neural-network libraries, collective communication, utility libraries, framework extensions, and custom operators. A framework being available on the destination does not automatically cover every library or extension used by your application.
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AMD documents HIP libraries corresponding to many CUDA libraries and recommends AMD-optimized roc libraries where available. Intel’s CUDA-to-SYCL migration guidance likewise treats library calls as part of the port: compatibility tools can automate some supported Thrust and CUB calls, while other calls need manual API mapping. Check the specific functions your code uses, not just the library name.
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What changes in the code, build, and deployment?
Kernel source and compilation
For custom CUDA kernels, identify unsupported language features, architecture-specific assumptions, inline assembly, and any platform-specific launch or memory behavior. AMD’s HIPIFY tooling can translate supported CUDA source patterns, but generated code still needs review and may require manual changes.
Build the result with the destination toolchain for the target GPU architecture. Update compiler options, link dependencies, build scripts, runtime configuration, and packaging as needed. AMD’s guidance notes that HIP code must be compiled for a specific AMD GPU architecture, so successful translation alone is not a completed port.
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Automated migration tools
HIPIFY and Intel’s SYCLomatic or Compatibility Tools can reduce repetitive conversion work for supported code. Treat their output as a starting point: inspect changes, resolve unsupported APIs, and verify that library calls and build settings are appropriate for the destination. The oneAPI specification describes source-level compatibility in terms of recompilation and re-tuning, not a promise that an application runs unchanged.
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After the workload works on one destination device, check the platform’s supported containers and deployment tools. AMD describes ROCm workflows involving containers, Kubernetes, Slurm, and multi-node environments; those capabilities do not prove that a particular application or image will transfer unchanged. Validate the deployment configuration you intend to operate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you migrate and verify the workload?
- Record a source-platform baseline. Save a known-working build, dependency versions, build steps, correctness tests and tolerances, and performance measurements. This gives you a reference for later comparisons.
- Map platform-specific dependencies. Find direct CUDA runtime or driver calls, CUDA-only libraries, custom kernels, inline assembly, architecture assumptions, framework extensions, and deployment dependencies.
- Select a supported target stack. Check the exact GPU, operating system, driver, accelerator release, framework, libraries, and deployment components against the destination’s current compatibility information.
- Port in small, testable pieces. Prefer supported destination framework or library implementations where they meet your needs. Use translation tools for supported patterns, review the results, and handle unsupported calls manually.
- Build for the target and resolve integration issues. Update compiler and linker settings, architecture targets, dependencies, packaging, and runtime configuration. Test each converted section as it becomes buildable.
- Validate correctness before tuning. Run application tests and compare numerical results against the source baseline using the workload’s established tolerances. Investigate changed outputs or behavior before treating the port as complete.
- Profile and tune on the destination. Measure the target workload, including compute and host-to-device data movement. Optimize for the destination architecture and remeasure; source-platform speed does not predict destination performance.
- Test the intended scale and deployment. Once single-device behavior is verified, measure multi-GPU and multi-node performance at the target configuration and validate the production deployment path.
How do you compare platforms for this workload?
There is no universal platform ranking that settles a migration decision. Compare the specific workload and operating requirements across these dimensions:
- Framework and model-serving support for the versions already in use.
- Coverage of required libraries, custom operators, and communication needs.
- GPU, operating system, driver, compiler, and accelerator-stack support.
- Effort to port and maintain custom kernels.
- Correctness and numerical behavior under the application’s tolerances.
- Measured throughput, latency, memory use, and scaling after profiling.
- Fit with the team’s deployment and operations environment.
Measure performance on the destination hardware and node count you intend to use. Vendor migration guidance supports profiling and tuning, but does not provide a workload-independent speed estimate or a basis for assuming that one platform will be faster for every AI application.
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