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- // Ceres Solver - A fast non-linear least squares minimizer
- // Copyright 2022 Google Inc. All rights reserved.
- // http://ceres-solver.org/
- //
- // Redistribution and use in source and binary forms, with or without
- // modification, are permitted provided that the following conditions are met:
- //
- // * Redistributions of source code must retain the above copyright notice,
- // this list of conditions and the following disclaimer.
- // * Redistributions in binary form must reproduce the above copyright notice,
- // this list of conditions and the following disclaimer in the documentation
- // and/or other materials provided with the distribution.
- // * Neither the name of Google Inc. nor the names of its contributors may be
- // used to endorse or promote products derived from this software without
- // specific prior written permission.
- //
- // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
- // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- // POSSIBILITY OF SUCH DAMAGE.
- //
- // Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
- #ifndef CERES_INTERNAL_CUDA_KERNELS_UTILS_H_
- #define CERES_INTERNAL_CUDA_KERNELS_UTILS_H_
- namespace ceres {
- namespace internal {
- // Parallel execution on CUDA device requires splitting job into blocks of a
- // fixed size. We use block-size of kCudaBlockSize for all kernels that do not
- // require any specific block size. As the CUDA Toolkit documentation says,
- // "although arbitrary in this case, is a common choice". This is determined by
- // the warp size, max block size, and multiprocessor sizes of recent GPUs. For
- // complex kernels with significant register usage and unusual memory patterns,
- // the occupancy calculator API might provide better performance. See "Occupancy
- // Calculator" under the CUDA toolkit documentation.
- constexpr int kCudaBlockSize = 256;
- // Compute number of blocks of kCudaBlockSize that span over 1-d grid with
- // dimension size. Note that 1-d grid dimension is limited by 2^31-1 in CUDA,
- // thus a signed int is used as an argument.
- inline int NumBlocksInGrid(int size) {
- return (size + kCudaBlockSize - 1) / kCudaBlockSize;
- }
- } // namespace internal
- } // namespace ceres
- #endif // CERES_INTERNAL_CUDA_KERNELS_UTILS_H_
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