| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 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_ |