Scientific Accelerator Portability

Scientific Accelerator Portability

Use this skill when scientific code targets GPUs or other accelerators and must remain correct, maintainable, and reasonably portable across hardware, compilers, drivers, and execution environments.

Repository AGENTS.md instructions take precedence over this skill.

Purpose

Apply a pragmatic default for:

• separating portable accelerator abstractions from backend-specific code;
• preserving CPU or non-accelerated fallback paths where practical;
• documenting hardware, driver, compiler, and runtime assumptions;
• testing correctness across backend and precision differences;
• avoiding overfitting to one GPU generation, vendor, or cluster.

When To Use

Use this skill when:

• code uses CUDA, HIP, SYCL, OpenACC, OpenMP offload, Kokkos, RAJA, OpenCL, or

accelerator-specific libraries;

• a CPU implementation is being ported to GPU or accelerator hardware;
• code must run on more than one accelerator backend or hardware generation;
• performance changes alter data movement, precision, launch shape, or memory

layout;

• CI, containers, or workflows need accelerator-aware smoke tests.

Do not use this skill as a substitute for benchmarking or profiling. Use scientific-cli-benchmark and scientific-profiling to measure behavior, then use this skill to reason about portability and fallback tradeoffs.

Working Approach

When this skill applies:

1. Read the repository AGENTS.md first, if present.
2. Identify the accelerator backend, target hardware, compiler, driver, and

runtime assumptions.

1. Find the baseline implementation and expected scientific outputs.
2. Preserve or document fallback behavior before specializing.
3. Review correctness, data movement, memory layout, precision, and launch

assumptions together.

1. State which backends or devices were considered, tested, and left untested.

Prefer portable abstractions and explicit backend boundaries before adding vendor-specific special cases.

Backend Strategy

• Use a portability layer such as Kokkos, RAJA, SYCL, OpenMP offload, or

OpenACC when the project already depends on it or needs multi-vendor support.

• Use CUDA or HIP directly when the project deliberately targets that backend

and the maintenance cost is accepted.

• Keep backend-specific code isolated behind capability checks, build options,

or narrow implementation files.

• Avoid mixing unrelated accelerator models in one code path without a clear

abstraction boundary.

• Document which backend is primary and which are best-effort.

Portability should be a design choice, not an accidental collection of compiler branches.

Fallback Paths

• Preserve a CPU or serial fallback when practical.
• Make fallback selection explicit through build options, runtime detection, or

documented configuration.

• Ensure tests cover fallback paths, not only the fastest accelerator path.
• Avoid silently changing scientific behavior when falling back to CPU or a

different precision.

• Document when no fallback exists and why.

A slow correct fallback is often valuable for validation and portability.

Data Movement and Memory

• Minimize host-device transfers in hot paths.
• Keep ownership and lifetime of device data explicit.
• Avoid hidden synchronization unless it is required for correctness.
• Check that memory layout matches access patterns for the target backend.
• Be explicit about unified memory, pinned memory, managed memory, and device

allocations.

• Treat out-of-memory behavior and allocation failures as expected failure

modes, not impossible states.

Data movement mistakes can dominate performance and obscure correctness bugs.

Precision and Numerical Behavior

• Check whether the accelerator path changes precision, reduction order,

math-library implementation, or fused operations.

• Use tolerances justified by algorithm, scale, backend, and reduction behavior.
• Compare accelerator and CPU outputs on small deterministic cases.
• Avoid assuming bitwise equality across GPU models, compiler versions, or math

libraries unless the project explicitly enforces it.

• Document when backend-specific numerical drift is expected.

Use scientific-numerics-review for deeper numerical stability and tolerance analysis.

Build and Configuration

• Keep accelerator backend selection explicit in CMake, package metadata, or

build scripts.

• Avoid hardcoding one CUDA architecture, GPU name, compiler path, or SDK

install location as a global default.

• Support architecture lists or configurable target capabilities when

reasonable.

• Separate required accelerator dependencies from optional acceleration.
• Fail clearly when a requested backend is unavailable.

Use scientific-build-systems for implementation details in compiled build configuration.

Containers and Workflows

• Document host driver and runtime requirements for GPU containers.
• Do not assume the container fully owns the GPU software stack.
• For Apptainer/Singularity, document GPU flags such as --nv or --rocm

where relevant.

• Keep workflow profiles explicit about accelerator requirements.
• Provide tiny accelerator smoke tests before large GPU runs.

Use scientific-container-workflows and scientific-workflow-automation for container and workflow integration.

CI and Testing

• Keep CPU/fallback tests in ordinary CI when accelerator runners are

unavailable.

• Add accelerator smoke tests only when suitable runners or self-hosted

infrastructure are available.

• Mark GPU-specific tests clearly so they can be skipped or selected.
• Test small deterministic inputs before performance-sized workloads.
• Check both correctness and device selection behavior.

CI should not pretend to validate accelerator support if no accelerator is available.

Performance Portability

• Do not overfit launch dimensions, block sizes, vector widths, or tile sizes to

one GPU generation without measurement.

• Prefer tunable parameters when one fixed value is unlikely to generalize.
• Distinguish occupancy, bandwidth, latency, transfer, and synchronization

bottlenecks.

• Record hardware model, driver, compiler, runtime, and input shape for

benchmark claims.

• Preserve maintainability unless the specialization has measured value.

Use scientific-performance-portability for broader non-accelerator portability tradeoffs.

Anti-Patterns

• making the accelerator path the only tested implementation;
• hiding backend selection in local environment variables;
• hardcoding one user's CUDA, ROCm, or compiler install path;
• assuming all GPUs support the same precision, atomics, memory, or libraries;
• committing code that only works on one cluster without documenting why;
• treating a successful kernel launch as proof of scientific correctness;
• using GPU timing without synchronizing or accounting for data transfer.

Validation Defaults

• Build or configure the requested backend when practical.
• Run a tiny correctness case on CPU/fallback and accelerator paths when

available.

• Check device discovery or backend-selection output.
• Compile or run tests for fallback behavior.
• Record which hardware, compiler, driver, and backend were actually tested.
• If no accelerator is available, state that only static/build-level checks were

performed.

Output Expectations

When using this skill, briefly note:

• which backend, hardware, compiler, driver, and runtime assumptions were

considered;

• whether CPU/fallback behavior exists and was tested;
• what correctness, tolerance, data-movement, or configuration risks were

addressed;

• which accelerator smoke tests, builds, or benchmarks were run;
• what remains backend-specific, hardware-specific, or untested.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: gjbex
• Source: gjbex/scientific-computing-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.