There is no single number that answers “how much faster is CUDA than OpenCL”. Most published gaps are measuring three things that have little to do with the APIs themselves: how much tuning effort each kernel received, how mature the vendor’s compiler is, and whether a hand-optimised vendor library (cuBLAS, cuDNN) sits behind one side of the comparison and generic code behind the other.
That distinction matters because architectural decisions get made on those headline percentages. A team reads that CUDA is “30% faster”, locks itself into single-vendor procurement, and never checks whether its own kernels behave that way.
Where does the measured gap actually come from?
The usual divergence point is the memory model. A kernel written against CUDA-specific memory patterns — shared-memory tiling sized to a particular warp width, coalescing assumptions baked into index arithmetic — and then mechanically translated to OpenCL will underperform. The result gets attributed to OpenCL rather than to the port.
Two things follow, and we see both regularly in audit work:
- Compute-bound kernels are where the gap can be real. Dense GEMM-style work leaning on cuBLAS or cuDNN is competing against years of vendor tuning, not against an API specification.
- Bandwidth-bound kernels are often within noise. When a kernel is limited by achieved memory bandwidth rather than instruction throughput, both APIs are queueing the same memory traffic against the same hardware, and the measured difference frequently sits inside run-to-run variance.
That second point is the one most headline comparisons obscure, because it makes the answer workload-dependent rather than quotable.e.e.
What to measure before deciding
Record these per kernel, on the hardware you actually intend to buy or keep:
| Metric | Why it decides the question |
|---|---|
| Kernel wall-clock time | The only number the workload feels; measure over repeated runs, not a single launch |
| Achieved memory bandwidth as % of peak | Separates bandwidth-bound kernels (gap usually small) from compute-bound ones (gap can be real) |
| Occupancy | Reveals whether a translated kernel is under-occupied because its launch geometry was inherited from CUDA |
| Which side calls a vendor library | If one path hits cuBLAS/cuDNN and the other hits hand-written code, you are benchmarking libraries, not APIs |
A comparison with those four columns filled in turns the API question into a quantified trade-off: an accepted percentage of throughput against the removal of single-vendor procurement constraints. Without them, the decision rests on someone else’s workload.
The honest framing
If a kernel is rewritten for OpenCL’s memory model rather than translated, and both sides are measured with the same tuning budget, the remaining difference is the one worth arguing about. Everything above that line is porting debt.
Our GPU acceleration and optimisation work treats this as a measurement task first — per-kernel timings on target hardware, then the portability discussion. The unresolved part is tuning budget: nobody has a clean way to declare how much optimisation effort each side received, which is precisely why cross-vendor benchmark tables stay contestable.
What percentage of throughput is your team actually willing to trade for multi-vendor hardware options — and have you measured whether you would be trading any at all?
