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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTo run one research project on NVIDIA and non-NVIDIA GPUs, make the source portable and build a separate executable for each GPU/software stack. Existing CUDA C++ is usually the least disruptive candidate for HIP and HIPIFY; new or heavily refactored C++ can use Kokkos; and directive-oriented C, C++ or Fortran may fit OpenMP offloading. None of these choices creates a universal binary or guarantees equal speed. You must compile, test and benchmark every target with its own compiler, runtime, libraries and GPU architecture.
What “portable across GPUs” actually means
GPU portability has three separate levels:
- Source portability: the same project source, perhaps with conditional configuration, can be compiled for different vendors.
- Functional portability: the resulting programs produce acceptable results on each target.
- Performance portability: each target meets a defined throughput, latency or time-to-solution goal.
A HIP source file can be compiled into an AMD executable and an NVIDIA executable, but those binaries are not interchangeable. Kokkos can present one programming model while selecting different device backends at configuration time. OpenMP directives can offload selected regions when the particular compiler and runtime support the target. Treat portability as a build-and-validation process, not as a one-time switch.
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The available evidence does not establish a universally fastest framework or a performance number that applies to all scientific workloads. A 2024 survey from the University of Maryland discusses methods for evaluating performance portability, but its existence is not proof that any one model wins every case (University of Maryland performance-portability study).
Start with an inventory of the code and systems
Before changing kernels, record what the project actually assumes. This prevents a migration tool from hiding a dependency that only works on the original machine.
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Inventory the software
- CUDA runtime, driver and toolkit versions.
- Compiler family and version, language standards and linker options.
- GPU libraries such as BLAS, FFT, sparse or communication components, including whether a vendor-specific implementation is required.
- Build-system files, generated code, architecture flags and continuous-integration jobs.
- Operating systems, container images and environment modules used by researchers and production runs.
Inventory CUDA-specific code
- Kernel launches, streams, events, memory pools and explicit device-pointer management.
- Warp- or vendor-specific intrinsics, inline PTX, texture or surface operations and cooperative-group assumptions.
- Compile-time architecture checks and runtime feature queries.
- Numerical assumptions about reduction order, fused operations, atomics, denormal handling and allowable tolerances.
- Third-party extensions that expose only CUDA interfaces.
AMD’s HIP FAQ says HIPIFY can automatically convert almost all CUDA runtime code, but it also identifies unsupported CUDA capabilities and architecture-feature queries as areas that may need manual adaptation. That makes an inventory essential even when an automated conversion appears clean.
Choose a portability route
| Route | Best starting point | Documented scope | Trade-off |
|---|---|---|---|
| HIP and HIPIFY | Existing CUDA C++ that must target AMD and NVIDIA | HIP is AMD’s C++ runtime and kernel language; HIPIFY converts much CUDA runtime code. | Review every generated change, unsupported API and architecture assumption. Build separately because binaries are not portable between vendors. |
| Kokkos | New code or a substantial C++ refactor | The configuration guide documents CUDA, HIP and SYCL device backends for NVIDIA, AMD and Intel GPUs. | The abstraction changes data and execution structure. A configuration enables only one device backend at a time, so each target needs an intentional build. |
| OpenMP offloading | Existing C, C++ or Fortran that fits directive-based parallelism | Intel’s 2025.0 guide describes GPU offload with OpenMP directives and tuning for transfers and allocations. | Support depends on the exact compiler, runtime, GPU and OpenMP features. Intel’s guide is not a universal compatibility table. |
| NVIDIA HPC SDK | NVIDIA-only HPC development in C, C++, Fortran, CUDA or OpenACC | NVIDIA lists compilers, GPU libraries, profiling and debugging tools, plus OpenACC and CUDA support. | It is useful within NVIDIA’s documented scope; adopting it alone does not make a program execute on AMD or Intel GPUs. |
Use the least disruptive route that meets the project’s targets. A CUDA migration normally starts with HIP/HIPIFY. A team able to redesign its C++ data structures may gain a cleaner long-term interface from Kokkos. Directive-based applications may avoid a kernel-language rewrite with OpenMP, provided the compiler and runtime combination has the required offload support.
Port CUDA C++ with HIP and HIPIFY
Understand what HIP changes
HIP keeps a CUDA-like C++ programming style while providing a runtime and kernel language intended for AMD platforms and also usable for NVIDIA targets. The HIP 7.0.51831 porting guide explicitly distinguishes AMD and NVIDIA target platforms and states that generated binaries are not portable between them. Your deliverable is therefore portable source plus reproducible target-specific builds, not one executable copied everywhere.
Run the automated conversion as a first pass
- Create a clean branch and preserve the original CUDA build so you can compare behavior.
- Run HIPIFY over source files and build metadata according to the current ROCm documentation.
- Compile the converted project for one target platform, then inspect the complete diff rather than accepting generated edits blindly.
- Search for CUDA APIs, intrinsics, inline assembly, architecture macros and external libraries that the converter did not replace.
- Record every manual substitution and the reason for it; this becomes the maintenance map for future toolkit changes.
HIPIFY’s “almost all CUDA runtime code” statement is AMD documentation, not a guarantee that an entire application ports without work. Kernels that rely on vendor-specific instructions, feature queries or libraries commonly need design decisions rather than text replacement.
Audit architecture and API assumptions
- Replace hard-coded compute-capability checks with target-appropriate feature detection or a documented configuration.
- Review warp-size, subgroup, synchronization and atomic assumptions instead of assuming identical execution behavior.
- Check memory alignment, address-space qualifiers, managed-memory behavior and peer-access expectations on each runtime.
- Map every CUDA-only library call to an implementation available on the intended target, or isolate that operation behind a backend interface.
- Retest numerical algorithms whose reduction order or math intrinsics can differ between devices.
Keep separate HIP build configurations
Define one build configuration per GPU family, compiler and runtime. Store the GPU architecture flags, compiler version, ROCm or CUDA runtime version, library versions and host compiler in the build record. Do not infer compatibility from a successful compile on one vendor’s stack. The same HIP source may require different flags, library paths or conditional code for AMD and NVIDIA.
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Use Kokkos when an abstraction is acceptable
What Kokkos provides
Kokkos separates much of the algorithm from the execution and memory spaces. Its configuration guide documents CUDA for NVIDIA, HIP for AMD and SYCL for Intel GPUs. This can be a good fit when the project is new, when kernels are being redesigned, or when a team can migrate data structures and parallel patterns rather than preserve every CUDA call.
Plan the refactor around execution and memory spaces
- Identify the project’s parallel loops, reductions, views, host/device mirrors and synchronization points.
- Express those operations through Kokkos abstractions instead of exposing raw CUDA launch details throughout the application.
- Keep backend-specific code in narrow adapters for operations that have no suitable abstraction.
- Configure and build one device backend at a time, then repeat for each target.
- Run the same tests and representative workloads after every backend change.
Kokkos’ configuration rule matters operationally: only one device backend can be enabled in a given build configuration. A single build is not simultaneously a CUDA, HIP and SYCL executable. Use separate build directories or containers and make the selected backend visible in logs.
What abstraction does not solve
An abstraction can reduce source duplication, but it does not remove backend differences in memory capacity, atomics, subgroup behavior, compiler maturity or library availability. You still need target-specific tuning and a test suite that catches numerical and synchronization errors. The performance-portability literature linked above is a reason to measure broadly, not a promise of equal results.
Apply OpenMP offloading to directive-oriented code
When directives are a good fit
OpenMP offloading can move selected loops or regions to a GPU while leaving the surrounding C, C++ or Fortran structure recognizable. It is attractive when the code already uses OpenMP, when a full kernel-language migration would be disruptive, or when the project’s compiler ecosystem has strong directive support.
Intel’s OpenMP Offloading Tuning Guide (version 2025.0) describes Intel LLVM-based C/C++/Fortran compilers, GPU offload and tuning around data transfers and memory allocation. Those statements should not be generalized to every OpenMP compiler or every non-Intel GPU.
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Make data movement explicit in your design
- Identify which arrays and objects are used repeatedly on the device.
- Keep data resident across multiple kernels when the runtime and compiler support it.
- Minimize repeated host-device transfers and unnecessary allocation or deallocation.
- Check pointer lifetimes, mapping clauses and synchronization points.
- Profile transfer time separately from kernel execution time.
A directive that compiles is not evidence that the intended work ran efficiently on the GPU. Confirm offload at runtime, inspect generated diagnostics where available and profile representative workloads.
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The NVIDIA HPC SDK bundles NVIDIA-focused compilers, GPU libraries, profiling and debugging tools, CUDA and OpenACC support. It can simplify an NVIDIA deployment, particularly for C, C++ and Fortran HPC applications. It is not a cross-vendor execution layer: choosing this SDK does not establish support for AMD or Intel GPUs. If non-NVIDIA execution is a requirement, pair an appropriate portability strategy with a separately supported toolchain.
A repeatable build and validation workflow
1. Define target combinations
Write a matrix listing each GPU model or architecture, operating system, compiler, accelerator runtime, math and communication libraries, and intended application version. Include the exact configuration in CI and in published results. Software support changes by release, so consult current versioned matrices before deployment.
2. Select and isolate the backend
Choose HIP/HIPIFY, Kokkos or OpenMP based on the code inventory. Keep backend-specific headers, compiler flags and libraries behind the build system. For Kokkos, create one configuration per device backend. For HIP, follow the AMD/NVIDIA target distinction in the porting guide.
3. Produce reproducible target builds
Build a fresh executable for every row in the matrix. Record source revision, compiler and runtime versions, architecture flags, dependency versions and build options. Never copy a binary built for one vendor and assume another driver can run it.
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4. Run correctness tests first
Use deterministic unit tests where possible, then end-to-end research cases with domain-appropriate numerical tolerances. Compare conserved quantities, residuals, error norms and output files rather than requiring bit-for-bit identity when parallel reduction order legitimately differs. Include edge cases, empty inputs, large allocations and multi-GPU paths if the application uses them.
5. Profile representative workloads
Measure kernel execution, host-device transfers, allocations, synchronization and I/O separately. Intel’s OpenMP guidance specifically highlights reducing transfers and allocations; the same categories are useful diagnostics for any backend. Profile enough workload to expose memory-capacity and scaling effects, not only a toy kernel.
6. Report functional and performance results separately
State whether each target passed correctness, then state the measured performance under the hardware and software conditions recorded in the matrix. “Runs” means functional portability; it does not imply equal throughput or cost. If a target misses the project’s performance goal, document that result instead of calling the code performance-portable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and fixes
The converter leaves compile errors
Classify each error as an unsupported API, an architecture assumption, a missing library or a build-system issue. Replace unsupported calls with a portable algorithm or backend adapter; do not repeatedly rerun the converter expecting it to invent semantics.
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Check uninitialized memory, race conditions, reduction order, atomics, floating-point modes and synchronization. Tighten tests around the first divergent intermediate result and use tolerances justified by the algorithm. A compiler warning-free build cannot prove numerical equivalence.
Performance collapses after porting
Profile before changing kernels. Excessive transfers, allocation churn, poor occupancy, unsupported library fallbacks or a memory-access pattern suited to the original GPU can dominate runtime. Optimize the measured bottleneck on the target rather than assuming a source-level abstraction is cost-free.
A Kokkos backend is missing or conflicts with another
Check the Kokkos configuration and project release, verify that the requested backend is supported for the selected architecture, and configure a clean build directory. Because one device backend is enabled per configuration, create separate configurations instead of trying to combine incompatible backend settings.
OpenMP directives run on the host
Verify compiler offload options, runtime libraries, device visibility and supported directive features for that exact compiler/GPU pair. Inspect runtime diagnostics and profile the application. Intel’s 2025.0 documentation cannot certify behavior for a different compiler or device.
A HIP binary will not run on another vendor’s GPU
Rebuild from the shared HIP source for the destination platform with its compiler, runtime and architecture settings. This is expected behavior under the HIP porting guide’s distinction between AMD and NVIDIA binaries.
Decision checklist
- Choose HIP/HIPIFY when preserving a substantial CUDA C++ codebase is the priority and AMD plus NVIDIA targets are required.
- Choose Kokkos when the team can refactor C++ data and execution patterns and wants documented CUDA, HIP and SYCL backend options.
- Choose OpenMP offloading when directives fit the existing C/C++/Fortran design and the exact compiler/runtime/GPU combination has the needed support.
- Use the NVIDIA HPC SDK for an NVIDIA-focused toolchain, not as evidence of non-NVIDIA portability.
Whichever route you choose, preserve a native reference implementation until the replacement passes correctness tests, and keep a separate build and benchmark record for every intended GPU stack. Portability is successful only when the code remains maintainable, produces scientifically acceptable results and meets the project’s stated performance target on each device.
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