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project_6_89d52222/cccl_upstream/cub/cub/thread/thread_search.cuh
EngineX CI 56fd68e7dd [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
2026-07-30 09:35:51 +00:00

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// SPDX-FileCopyrightText: Copyright (c) 2011, Duane Merrill. All rights reserved.
// SPDX-FileCopyrightText: Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
// SPDX-License-Identifier: BSD-3
/**
* @file
* Thread utilities for sequential search
*/
#pragma once
#include <cub/config.cuh>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/util_namespace.cuh>
#include <cub/util_type.cuh>
#include <cuda/std/__algorithm/max.h>
#include <cuda/std/__algorithm/min.h>
#include <cuda/std/__floating_point/cuda_fp_types.h>
#include <nv/target>
CUB_NAMESPACE_BEGIN
/**
* Computes the begin offsets into A and B for the specific diagonal
*
* Deprecated [Since 3.0]
*/
template <typename AIteratorT, typename BIteratorT, typename OffsetT, typename CoordinateT>
CCCL_DEPRECATED _CCCL_HOST_DEVICE _CCCL_FORCEINLINE void MergePathSearch(
OffsetT diagonal, AIteratorT a, BIteratorT b, OffsetT a_len, OffsetT b_len, CoordinateT& path_coordinate)
{
/// The value type of the input iterator
using T = cub::detail::it_value_t<AIteratorT>;
OffsetT split_min = ::cuda::std::max(diagonal - b_len, 0);
OffsetT split_max = ::cuda::std::min(diagonal, a_len);
while (split_min < split_max)
{
OffsetT split_pivot = (split_min + split_max) >> 1;
if (a[split_pivot] <= b[diagonal - split_pivot - 1])
{
// Move candidate split range up A, down B
split_min = split_pivot + 1;
}
else
{
// Move candidate split range up B, down A
split_max = split_pivot;
}
}
path_coordinate.x = ::cuda::std::min(split_min, a_len);
path_coordinate.y = diagonal - split_min;
}
/**
* @brief Returns the offset of the first value within @p input which does not compare
* less than @p val
*
* @param[in] input
* Input sequence
*
* @param[in] num_items
* Input sequence length
*
* @param[in] val
* Search key
*/
// TODO(bgruber): deprecate once ::cuda::std::lower_bound is made public
template <typename InputIteratorT, typename OffsetT, typename T>
_CCCL_DEVICE _CCCL_FORCEINLINE OffsetT LowerBound(InputIteratorT input, OffsetT num_items, T val)
{
OffsetT retval = 0;
while (num_items > 0)
{
OffsetT half = num_items >> 1;
if (input[retval + half] < val)
{
retval = retval + (half + 1);
num_items = num_items - (half + 1);
}
else
{
num_items = half;
}
}
return retval;
}
/**
* @brief Returns the offset of the first value within @p input which compares
* greater than @p val
*
* @param[in] input
* Input sequence
*
* @param[in] num_items
* Input sequence length
*
* @param[in] val
* Search key
*/
// TODO(bgruber): deprecate once ::cuda::std::upper_bound is made public
template <typename InputIteratorT, typename OffsetT, typename T>
_CCCL_DEVICE _CCCL_FORCEINLINE OffsetT UpperBound(InputIteratorT input, OffsetT num_items, T val)
{
OffsetT retval = 0;
while (num_items > 0)
{
OffsetT half = num_items >> 1;
if (val < input[retval + half])
{
num_items = half;
}
else
{
retval = retval + (half + 1);
num_items = num_items - (half + 1);
}
}
return retval;
}
#if _CCCL_HAS_NVFP16()
/**
* @param[in] input
* Input sequence
*
* @param[in] num_items
* Input sequence length
*
* @param[in] val
* Search key
*/
template <typename InputIteratorT, typename OffsetT>
_CCCL_DEVICE _CCCL_FORCEINLINE OffsetT UpperBound(InputIteratorT input, OffsetT num_items, __half val)
{
OffsetT retval = 0;
while (num_items > 0)
{
OffsetT half = num_items >> 1;
bool lt;
NV_IF_ELSE_TARGET(NV_PROVIDES_SM_53,
(lt = __hlt(val, input[retval + half]);),
(lt = __half2float(val) < __half2float(input[retval + half]);));
if (lt)
{
num_items = half;
}
else
{
retval = retval + (half + 1);
num_items = num_items - (half + 1);
}
}
return retval;
}
#endif // _CCCL_HAS_NVFP16()
#if _CCCL_HAS_NVBF16()
/**
* @param[in] input
* Input sequence
*
* @param[in] num_items
* Input sequence length
*
* @param[in] val
* Search key
*/
template <typename InputIteratorT, typename OffsetT>
_CCCL_DEVICE _CCCL_FORCEINLINE OffsetT UpperBound(InputIteratorT input, OffsetT num_items, __nv_bfloat16 val)
{
OffsetT retval = 0;
while (num_items > 0)
{
OffsetT half = num_items >> 1;
bool lt;
NV_IF_ELSE_TARGET(NV_PROVIDES_SM_80,
(lt = __hlt(val, input[retval + half]);),
(lt = __bfloat162float(val) < __bfloat162float(input[retval + half]);));
if (lt)
{
num_items = half;
}
else
{
retval = retval + (half + 1);
num_items = num_items - (half + 1);
}
}
return retval;
}
#endif // _CCCL_HAS_NVBF16()
CUB_NAMESPACE_END