Both SYCL and CUDA are single-source C++ programming models, so the difference looks like a namespace swap and a compiler flag. It is not. The two models put memory ownership and scheduling decisions in different places, and that — not the kernel body — is where a port succeeds or quietly fails.
CUDA gives you an explicit device pointer and an explicit stream. You own the allocation, the copy, and the ordering. SYCL abstracts device selection behind a queue, and its buffer/accessor model tracks data dependencies for you so the same source can be compiled for NVIDIA, AMD, and Intel back ends. The kernel arithmetic usually translates almost mechanically. The assumptions wrapped around it do not.
How do the SYCL and CUDA memory models differ in ways that affect performance?
Translating syntax does not translate portability. Code written against CUDA-specific memory assumptions — pinned host allocations, shared-memory tiling sized to one SM generation, stream-ordered allocators — carries those assumptions across the port intact. The compiler accepts them. The second vendor’s hardware does not reward them.
This is why teams that treat SYCL as “CUDA with a different namespace” discover the cost during performance validation on the second target device, rather than during the port itself. The port compiles and runs; the throughput does not hold. In our experience reviewing GPU codebases, the surprise almost always sits in the data-movement layer and the tile sizes, not in the maths.
What translates and what needs a rewrite
| Element | Mechanical translation | Needs rewrite / re-tuning |
|---|---|---|
| Kernel arithmetic, control flow | Yes, largely | — |
| Thread/work-item indexing | Yes (nd_range maps cleanly) |
— |
Explicit cudaMemcpy / stream ordering |
No | Re-express as buffer/accessor dependencies or USM |
| Pinned host allocations | No | Re-evaluate per back end |
| Shared-memory tiling sized to one SM generation | No | Re-tune per target device |
| Vendor library calls (cuBLAS, cuDNN) | No | Map to equivalent per-vendor libraries |
| Occupancy and launch-config tuning | No | Re-measure on each device |
The output that makes the decision defensible
The useful deliverable from a SYCL-versus-CUDA evaluation is a per-kernel decision record: which kernels are portable at acceptable cost, and which are vendor-pinned. Each entry carries kernel-level runtime measured on each target device rather than assumed, plus engineer-days estimated per rewrite and the achieved percentage of the CUDA baseline throughput on non-NVIDIA hardware.
Without that record, the portability line in a hardware plan is an untested claim. With it, “we could go multi-vendor” becomes a number of kernels, a number of days, and a throughput figure someone can argue with. We treat that record as the precondition for recommending any API change during a GPU performance and optimisation review — the API is not the decision, the measured per-kernel cost is.
Staying on CUDA remains a defensible engineering decision when the kernel set is small, deeply tuned to one architecture, and the roadmap has no second vendor in it. The question worth asking before the port, not after: which of your kernels would still hit their latency budget if the shared-memory tile size were wrong?
