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LLVM support for the attribute has been implemented already, so it just plumbs it through to the CUDA front-end. One notable difference from NVCC is that the attribute can be used regardless of the targeted GPU. On the older GPUs it will just be ignored. The attribute is a performance hint, and does not warrant a hard error if compiler can't benefit from it on a particular GPU variant.
34 lines
1.5 KiB
Plaintext
34 lines
1.5 KiB
Plaintext
// RUN: %clang_cc1 -fsyntax-only -verify %s
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// RUN: %clang_cc1 -fsyntax-only -fcuda-is-device -verify %s
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#include "Inputs/cuda.h"
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struct S {};
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__global__ void kernel_struct(__grid_constant__ const S arg) {}
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__global__ void kernel_scalar(__grid_constant__ const int arg) {}
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__global__ void gc_kernel_non_const(__grid_constant__ S arg) {} // expected-error {{__grid_constant__ is only allowed on const-qualified kernel parameters}}
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void non_kernel(__grid_constant__ S arg) {} // expected-error {{__grid_constant__ is only allowed on const-qualified kernel parameters}}
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// templates w/ non-dependent argument types get diagnosed right
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// away, without instantiation.
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template <typename T>
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__global__ void tkernel_nd_const(__grid_constant__ const S arg, T dummy) {}
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template <typename T>
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__global__ void tkernel_nd_non_const(__grid_constant__ S arg, T dummy) {} // expected-error {{__grid_constant__ is only allowed on const-qualified kernel parameters}}
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// dependent arguments get diagnosed after instantiation.
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template <typename T>
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__global__ void tkernel_const(__grid_constant__ const T arg) {}
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template <typename T>
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__global__ void tkernel(__grid_constant__ T arg) {} // expected-error {{__grid_constant__ is only allowed on const-qualified kernel parameters}}
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void foo() {
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tkernel_const<const S><<<1,1>>>({});
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tkernel_const<S><<<1,1>>>({});
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tkernel<const S><<<1,1>>>({});
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tkernel<S><<<1,1>>>({}); // expected-note {{in instantiation of function template specialization 'tkernel<S>' requested here}}
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}
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