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To run one research project on NVIDIA and non-NVIDIA GPUs, make the source portable, then build and validate it separately for every vendor stack. Existing CUDA C++ usually starts with HIP and HIPIFY; new or heavily refactored C++ can use Kokkos; directive-oriented C, C++ or Fortran may fit OpenMP offloading. None of these makes one universal binary, and none guarantees equal speed. Treat portability as three separate jobs: adapting the source, producing a target-specific build, and proving correctness and performance on each system.
What “portable across GPUs” actually means
GPU portability has several levels, and confusing them causes most failed projects:
- Source portability: the same or closely related source code can be compiled for more than one vendor.
- Build portability: each target has a supported compiler, runtime, libraries and architecture configuration.
- Binary portability: one compiled executable can run unchanged on different vendors. HIP documentation explicitly separates AMD and NVIDIA targets and states that their generated binaries are not interchangeable (AMD ROCm HIP Porting Guide 7.0.51831).
- Functional portability: the program produces acceptable results on every target.
- Performance portability: it also meets a defined time, throughput, memory or energy target on every target.
The practical goal is therefore usually one maintained codebase with a reproducible build for each GPU family, not one binary that ignores vendor differences. The performance-portability literature warns against broad conclusions from narrow case studies; measure your own workload and state the hardware and software context when reporting results (University of Maryland, “Taking GPU Programming Models to Task for Performance Portability,” 2024 record).
Start with a portability inventory
Before changing kernels, write down what the application assumes. This inventory determines whether a mechanical translation is realistic or whether a programming-model change is cheaper.
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Record the target matrix
- GPU vendor, model and architecture for every deployment system.
- Operating system and host CPU architecture.
- Compiler, GPU compiler component, runtime and standard-library versions.
- Math, communication, FFT, sparse and other third-party GPU libraries.
- Required numerical precision, reproducibility expectations and acceptable tolerances.
- Build generator, package manager, continuous-integration runners and deployment method.
Find vendor-bound code
Search the source and build files for CUDA runtime and driver calls, CUDA libraries, device intrinsics, inline assembly, launch syntax, stream and event handling, unified-memory assumptions, device-pointer checks, architecture feature queries and conditional compilation. Also inspect code that assumes a particular warp or subgroup width, cache behavior, atomics, tensor instructions or memory-ordering rule. These assumptions often compile successfully after a translation but still produce wrong results or poor performance.
Separate interfaces from kernels
Map each GPU-facing operation to a small internal interface: allocation, copies, synchronization, kernel launch, error handling and library calls. Keeping that interface narrow lets you replace an implementation without rewriting the scientific algorithm. Do not hide every vendor distinction behind an abstraction prematurely; retain an escape hatch for a target-specific kernel where profiling proves it is needed.
Choose the least disruptive strategy
| Strategy | Best starting point | What the cited documentation establishes | Main trade-off |
|---|---|---|---|
| HIP and HIPIFY | Existing CUDA C++ that must reach AMD and NVIDIA | HIP is AMD’s C++ runtime and kernel language. AMD says HIPIFY can automatically convert almost all CUDA runtime code to HIP. | Generated changes require review; unsupported CUDA features, architecture queries and vendor libraries can still need manual work. Build separately for AMD and NVIDIA because binaries are not interchangeable. |
| Kokkos | New code or a substantial C++ refactor | The configuration guide documents CUDA, HIP and SYCL backends, covering NVIDIA, AMD and Intel GPU paths. | You change code structure and must configure one device backend per build; abstraction does not remove tuning or validation. |
| OpenMP offloading | Existing C, C++ or Fortran where directives fit the team and compiler | Intel’s 2025.0 guide describes GPU offload with OpenMP directives and tuning for transfers and allocations. | Support is compiler-, runtime- and target-specific. Intel’s guide is not a universal compatibility matrix. |
| NVIDIA HPC SDK | Projects whose deployment scope is NVIDIA GPUs, especially HPC C, C++ or Fortran | NVIDIA lists compilers, GPU libraries, profiling and debugging tools, plus OpenACC and CUDA support. | It is useful inside its documented NVIDIA scope; selecting it alone does not provide AMD or Intel execution. |
No source establishes one approach as universally best. Select based on how much source restructuring you can afford, which vendors you must support, and whether your existing libraries have equivalents on every target.
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What HIP changes
HIP provides a C++ programming model intended to let code target AMD or NVIDIA platforms. HIPIFY performs source-level conversions, especially for CUDA runtime code. AMD’s current HIP FAQ says HIPIFY “can automatically convert almost all CUDA runtime code to HIP” (AMD ROCm HIP FAQ, HIP 7.16 page accessed October 13, 2026). “Almost all” is not “every application”: kernels, libraries, intrinsics, architecture checks and build logic can still require engineering.
A controlled migration sequence
- Freeze a baseline. Record the CUDA compiler and libraries, GPU architecture, test data, numerical tolerances and representative timings. Keep the original CUDA build available until the port is trusted.
- Inventory unsupported surfaces. Mark driver APIs, specialized CUDA libraries, inline PTX or other assembly, architecture feature queries, warp-level assumptions and custom allocators. The HIP porting guide specifically cautions that CUDA features may need manual adaptation.
- Run HIPIFY on a branch. Treat generated edits as a starting point, not a code review exemption. Inspect every changed include, API name, type, launch expression and error path.
- Replace nonportable dependencies. Choose a HIP-capable library, a portable implementation, or a small backend interface for each CUDA-only dependency. If no equivalent exists, isolate that operation and document its target restrictions.
- Compile an AMD target and an NVIDIA target independently. Select the appropriate compiler, runtime and GPU architecture in each build. Do not copy an AMD binary into an NVIDIA environment or the reverse; the HIP guide says those binaries are not interchangeable.
- Run tests before optimizing. Compare outputs against the baseline with tolerances appropriate to the algorithm. A successful compile or kernel launch does not prove scientific correctness.
- Profile each build. Measure kernel time, host-device transfers, synchronization, allocation and library calls. Keep target-specific optimizations behind clear interfaces so the common algorithm remains maintainable.
HIP porting traps
- Architecture queries: code that branches on CUDA capability numbers may need a different feature test or a redesigned dispatch decision.
- Intrinsics and warp behavior: an intrinsic with the same-looking name may have different supported widths, ordering or performance characteristics.
- Library coverage: translating a runtime call does not translate every CUDA-optimized math, sparse or communication library.
- Build assumptions: architecture flags, compiler definitions and library paths are target configuration, not portable source. Keep them in per-target build profiles.
Use Kokkos when the code can adopt an abstraction
Kokkos expresses parallel patterns and memory spaces through C++ abstractions, while selecting a device backend during configuration. Its configuration documentation lists CUDA for NVIDIA, HIP for AMD and SYCL for Intel GPU targets (Kokkos Core Wiki Configuration Guide). This makes it a candidate for new research software or a major refactor, rather than a promise of an automatic CUDA conversion.
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When Kokkos is a good fit
- The team controls the C++ architecture and can replace direct kernel-launch code.
- Algorithms can be expressed with portable parallel loops, reductions, scans and views.
- You want one algorithmic implementation while retaining backend-specific tuning points.
- The project can maintain separate configured builds for the devices used by developers, CI and production.
Configuration implications
The Kokkos guide says only one device backend can be enabled in a given build configuration. Plan a build matrix rather than trying to produce one configuration that simultaneously activates every device backend. A typical matrix records the selected backend, compiler, GPU architecture and enabled optional packages for each job. Build and test every matrix entry; a host-only build cannot validate device behavior.
Costs and limits of abstraction
Abstraction changes data-layout choices, execution policies, synchronization points and debugging workflows. It can reduce duplicated kernels, but it does not make memory traffic free or guarantee equal occupancy across vendors. Keep performance-critical sections measurable and allow a backend-specific implementation when a portable version misses the required target.
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OpenMP offloading attaches directives to regions that should execute on a device, allowing teams to retain C, C++ or Fortran structure. Intel’s 2025.0 OpenMP Offloading Tuning Guide covers Intel LLVM-based compilers, GPU execution and tuning of data transfers and memory allocations.
Check support before converting loops
- Confirm that the exact compiler version supports the OpenMP constructs and target GPU you need.
- Check runtime support for device data mapping, reductions, atomics, asynchronous work and the language features used by your code.
- Verify that required math and communication libraries are available on the target stack.
- Test a small representative region before annotating the whole application.
Do not treat Intel’s guide as a compatibility promise for every OpenMP implementation or every vendor GPU. OpenMP is a specification, but compiler maturity and runtime behavior determine what your deployment can actually use.
Control data movement
OpenMP offload can underperform when arrays repeatedly cross the host-device boundary. Structure a data region around a meaningful unit of work, reuse device-resident allocations and avoid unnecessary synchronization. Profile allocation and transfer events as well as kernel execution; the Intel guide identifies both transfer reduction and allocation tuning as important concerns.
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Know what the NVIDIA HPC SDK can and cannot solve
The NVIDIA HPC SDK bundles compilers, GPU libraries, profiling and debugging tools, and supports CUDA and OpenACC workflows for NVIDIA GPUs. It can simplify a consistent NVIDIA build and provide strong tooling for that scope. It does not, by itself, make an application execute on AMD or Intel hardware. If non-NVIDIA execution is a requirement, pair a genuinely portable source strategy with a separate toolchain and target build.
Build a reproducible multi-target pipeline
Keep target configuration explicit
Create one named configuration per vendor and architecture. Record compiler and runtime versions, selected backend, GPU architecture, third-party library versions and relevant environment settings in build artifacts. Avoid relying on whichever GPU happens to be visible on a developer workstation.
Use a staged CI process
- Host checks: compile common code, run static analysis and execute fast algorithm tests without a GPU where possible.
- Target compilation: build every supported GPU configuration from the same revision.
- Device correctness: run deterministic smoke tests, edge cases, reductions, error paths and representative numerical workloads.
- Regression testing: compare outputs to approved tolerances and retain failure artifacts, compiler logs and device identification.
- Performance checks: run stable representative workloads and track kernel, transfer, allocation and end-to-end timings per target.
Keep separate caches and artifacts for each backend. A passing AMD job does not certify the NVIDIA build, and a host-only test does not exercise device memory behavior.
Validate correctness before claiming portability
Use scientific, not merely bitwise, comparisons
Different GPU instructions, reduction orders and math libraries can produce small floating-point differences. Define tolerances from the research method: absolute and relative error, conservation constraints, residuals, confidence intervals or another domain-appropriate criterion. Require exact equality only where the algorithm genuinely demands it.
Test failure-prone operations
- Non-aligned and unusual tensor or matrix sizes.
- Empty inputs, odd dimensions and boundary tiles.
- Large allocations and repeated allocation/free cycles.
- Concurrent streams, asynchronous copies and explicit synchronization.
- Atomics, reductions and algorithms whose order affects numerical stability.
- Fallback paths selected by device capability or memory size.
Record the environment with every result
Store GPU model and architecture, operating system, compiler and runtime versions, library versions, build options, input characteristics and measured tolerances. This is essential for distinguishing a code regression from a toolchain or hardware difference.
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Measure performance portability instead of assuming it
Run the same scientifically representative workload on every target, but interpret results in context. A kernel that is fastest on one vendor may be limited by memory bandwidth, launch overhead, occupancy, synchronization or a library implementation on another. Report end-to-end time alongside kernel time; otherwise a fast kernel can hide expensive transfers or allocations.
Profile these categories separately
- Host-to-device and device-to-host transfer volume and duration.
- Device allocation, deallocation and implicit synchronization.
- Kernel execution time and achieved utilization.
- Library calls, launch configuration and synchronization frequency.
- Peak memory use and behavior near capacity.
Define the performance target before tuning: for example, a maximum wall-clock time, minimum throughput or acceptable slowdown relative to a baseline. Do not publish a generic “portable performance” claim from one workload or one device pair; the cited performance-portability study is a reminder that conclusions are sensitive to the cases measured.
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It compiles for one vendor but not the other
- Check for an untranslated CUDA API, intrinsic, inline assembly block or library include.
- Inspect architecture feature queries and conditional compilation.
- Confirm that the target compiler and runtime versions support the API you selected.
- Replace a vendor-only dependency or isolate it behind a backend interface.
Both builds run, but results differ
- Run small deterministic inputs and identify the first operation whose output diverges.
- Check reduction order, atomics, synchronization and out-of-bounds accesses.
- Verify pointer ownership, mapping and lifetime across asynchronous regions.
- Use domain-appropriate tolerances; do not mask a large error with an overly broad threshold.
The portable version is much slower
- Separate transfer and allocation time from kernel time.
- Check whether a data region ends too frequently or forces synchronization.
- Compare memory layout, launch configuration and vectorization choices.
- Profile library calls and substitute a target-supported optimized routine where justified.
- Retain a common algorithm but add a narrowly scoped target-specific kernel if measurements justify its maintenance cost.
A binary works on one machine but not another
Rebuild for the destination GPU architecture and use the matching runtime and libraries. A portable source strategy still requires target-specific artifacts; HIP’s porting guide explicitly says AMD and NVIDIA binaries are not interchangeable.
A practical decision framework
Choose HIP/HIPIFY when
Your code is already CUDA C++, the algorithm and kernel structure are sound, and AMD support is the immediate requirement. Budget engineering time for manual review and library replacement after automated conversion.
Choose Kokkos when
You are starting a new C++ project or already planning a major redesign, and a maintained abstraction is preferable to parallel vendor implementations. Accept separate backend configurations and continued device-specific tuning.
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Choose OpenMP offloading when
Your team works primarily in C, C++ or Fortran, directives fit the code organization, and your exact compiler/runtime/GPU combination has the required support. Make data lifetime and transfer behavior explicit in performance work.
Use an NVIDIA-focused SDK when
Your deployment requirement is NVIDIA-only or you need a strong NVIDIA development and profiling environment alongside another portability path. Do not count it as the non-NVIDIA solution.
What to document for users and collaborators
A credible portability statement should name the tested GPU models and architectures, operating systems, compiler and runtime versions, libraries, build configurations, correctness tolerances and representative performance results. Say “source builds on AMD and NVIDIA” only when both builds have been tested. Say “performance portable” only with a defined performance goal and measured evidence on each target. These precise claims are more useful than an unqualified promise that the code runs everywhere.
Frequently Asked Questions
Can one HIP binary run on both AMD and NVIDIA GPUs?
No. The AMD HIP Porting Guide 7.0.51831 states that projects target AMD or NVIDIA platforms and that generated binaries are not portable between them. Maintain separate target builds from the portable source.
Does HIPIFY eliminate all manual CUDA porting?
No. AMD’s HIP FAQ says HIPIFY can automatically convert almost all CUDA runtime code, but unsupported APIs, architecture-feature queries, specialized libraries and low-level assumptions can still require manual changes.
Can Kokkos enable CUDA, HIP and SYCL in one build?
The Kokkos configuration guide says only one device backend can be enabled at a time in a given build configuration. Use separate configurations and test each one.
Is OpenMP offloading automatically portable to every GPU vendor?
No. OpenMP support depends on the exact compiler, runtime, GPU and supported constructs. Intel’s 2025.0 offloading guide documents Intel’s toolchain and should not be treated as a universal compatibility table.
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What is the minimum evidence for claiming GPU portability?
Build and run the same correctness suite on every intended target, record the complete software and hardware context, and measure representative end-to-end workloads. Distinguish functional portability from a stated performance target.
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