[INFRA] Import NVIDIA/CCCL upstream as optimization reference library
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
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//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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// Verify TEST_WORKAROUND_C1XX_BROKEN_ZA_CTOR_CHECK.
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#include <cuda/std/type_traits>
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#include "test_macros.h"
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#include "test_workarounds.h"
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struct X
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{
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TEST_FUNC X(int) {}
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X(X&&) = default;
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X& operator=(X&&) = default;
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private:
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X(const X&) = default;
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X& operator=(const X&) = default;
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};
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TEST_FUNC void PushFront(X&&) {}
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template <class T = int>
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TEST_FUNC auto test(int) -> decltype(PushFront(cuda::std::declval<T>()), cuda::std::true_type{});
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TEST_FUNC auto test(long) -> cuda::std::false_type;
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int main(int, char**)
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{
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#if defined(TEST_WORKAROUND_C1XX_BROKEN_ZA_CTOR_CHECK)
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static_assert(!decltype(test(0))::value);
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#else
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static_assert(decltype(test(0))::value);
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#endif
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return 0;
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}
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