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How much you need to change depends on how directly your workload uses the source GPU platform. An application built around a supported AI framework may keep much of its model and application code, provided the destination has a compatible framework build and software stack. Code that calls CUDA APIs or libraries, or includes custom CUDA kernels, usually needs more work: audit and adapt dependencies, build for the destination, validate correctness, and tune performance.
Start by finding out how tightly the workload is tied to its GPU
“Moving an AI workload” can mean moving a model and application between environments, or porting the GPU-specific code beneath them. Those are different tasks. Inventory the workload before estimating the change: framework and version, GPU APIs, libraries, custom operators and kernels, compiler and build settings, and deployment method.
| Workload component | What to check on the destination | Likely migration work |
|---|---|---|
| Framework-level model and application | A compatible framework build for the destination GPU, operating system, driver, and accelerator software release | Potentially limited source edits, but environment and framework compatibility still need verification. |
| CUDA runtime or driver calls | Equivalent destination APIs and behavior for the features the application uses | Audit and adapt API usage; a mechanical rename may not preserve behavior. |
| GPU libraries and custom operators | Destination libraries or supported equivalents for every required operation | Map calls to available libraries, update extensions, or implement unsupported cases. |
| Custom CUDA kernels | Destination language, compiler, and target GPU architecture | Translate or rewrite supported code, then compile, test, and tune for the target. |
| Build and deployment | Supported compiler, packaging, container, scheduler, and multi-GPU or multi-node setup | Update build and deployment configuration and verify behavior at the intended scale. |
AMD’s ROCm documentation lists frameworks such as PyTorch, TensorFlow, and JAX, while warning that support depends on the ROCm release and system configuration. Check the live compatibility matrix for the exact GPU, operating system, framework, and software-stack versions you plan to use; do not infer support from a framework name alone.
Which code and dependencies usually need edits?
Frameworks and the software environment
First establish a supported combination of GPU, operating system, driver, accelerator stack, and framework version. Even if model and application code do not change, the framework package or its GPU backend may need to change. Extensions, custom operators, and other components compiled against the source environment also need their own compatibility check.
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Runtime and driver APIs
CUDA and HIP are distinct platforms, not interchangeable interfaces. AMD’s HIP migration guidance describes differences in API organization, context behavior, memory copies, and module management. If the application uses low-level driver features, inspect those calls and their assumptions individually rather than relying on a broad text replacement.
Libraries and communication dependencies
List every GPU library the workload calls, including math, neural-network, collective-communication, and utility libraries. A translated kernel does not migrate a library dependency by itself. Intel’s CUDA-to-SYCL guidance covers supported automated conversion for some Thrust and CUB calls, with manual mapping needed for other cases. AMD documents HIP libraries corresponding to many CUDA libraries and recommends AMD-optimized roc libraries where available. Verify the destination equivalent for each dependency and the operations your workload actually uses.
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Kernels, compiler settings, and builds
HIPIFY and Intel’s SYCLomatic/Compatibility Tools can help translate supported CUDA source patterns, but generated code still needs review. Unsupported calls may require manual changes. The destination toolchain must compile the result for its target; AMD says HIP code must be compiled for a specific AMD GPU architecture. Include compiler flags, link dependencies, build scripts, packaging, and any inline assembly or architecture-specific assumptions in the migration audit.
How to migrate without mixing correctness and performance problems
- Record a source-platform baseline. Save working tests, numerical tolerances, dependency versions, build steps, and performance measurements for the source GPU, software versions, and workload configuration.
- Inventory platform-specific code. Search the application and its extensions for CUDA runtime and driver calls, CUDA-only libraries, custom kernels, inline assembly, architecture assumptions, and framework extensions.
- Select a verified destination stack. Check the vendor’s current compatibility information for the exact GPU, operating system, driver, accelerator software, and framework versions before settling on versions.
- Port from the highest supported layer downward. Prefer a destination-supported framework or library implementation where it covers the operation. Use a migration tool only for the code patterns it supports, then inspect the output and manually handle gaps.
- Build for the target and resolve integration issues. Update APIs, compiler options, link dependencies, build scripts, packaging, and runtime configuration until the workload builds and runs on the destination.
- Validate behavior before tuning. Run correctness tests and compare numerical outputs against the application’s established tolerances. Check application behavior as well as kernel results.
- Profile and optimize on the destination. Measure kernels and host-to-device data movement, identify bottlenecks, and tune for the target architecture. Re-measure using the intended GPU and node count; source-platform performance is not a reliable prediction of destination performance.
AMD recommends starting with a working CUDA codebase and porting incrementally, testing each converted section. Intel’s oneAPI specification describes source-level compatibility as requiring recompilation and retuning on a conformant oneAPI platform. In practice, translation is a starting point, not evidence that the complete application is compatible or equally fast.
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After a single-device run passes correctness checks, verify the destination’s supported containers and deployment tools, then test the workload at the GPU count and node count you intend to operate. AMD documents ROCm workflows involving containers, Kubernetes, Slurm, and multi-node environments; the existence of those platform capabilities does not establish that a particular application or its deployment configuration will work unchanged.
For a platform decision, compare the versions and capabilities your workload needs: framework and model-serving support, required libraries and custom operators, driver and operating-system compatibility, kernel-porting effort, numerical behavior within your tolerances, measured throughput, latency and memory use, scaling, and operational fit. Vendor documentation can establish support and migration guidance, but it does not provide a universal ranking across those workload-specific factors.
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What you can and cannot assume about migration effort
- A framework-oriented workload may require fewer source changes than one built around custom CUDA code, but the destination framework and environment still need to be supported together.
- Direct API, library, and kernel dependencies are strong signals of porting work. Automated conversion covers supported patterns, not every application dependency.
- Correctness and speed are separate acceptance criteria: establish behavior first, then profile and tune on the destination.
- There is no supported universal migration-time estimate or workload-independent performance figure here. Effort and results depend on the code, destination stack, and target hardware.
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