cuda_kernels_utils.h 2.8 KB

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  1. // Ceres Solver - A fast non-linear least squares minimizer
  2. // Copyright 2022 Google Inc. All rights reserved.
  3. // http://ceres-solver.org/
  4. //
  5. // Redistribution and use in source and binary forms, with or without
  6. // modification, are permitted provided that the following conditions are met:
  7. //
  8. // * Redistributions of source code must retain the above copyright notice,
  9. // this list of conditions and the following disclaimer.
  10. // * Redistributions in binary form must reproduce the above copyright notice,
  11. // this list of conditions and the following disclaimer in the documentation
  12. // and/or other materials provided with the distribution.
  13. // * Neither the name of Google Inc. nor the names of its contributors may be
  14. // used to endorse or promote products derived from this software without
  15. // specific prior written permission.
  16. //
  17. // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  18. // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  19. // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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  21. // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  22. // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  23. // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  24. // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  25. // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  26. // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  27. // POSSIBILITY OF SUCH DAMAGE.
  28. //
  29. // Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
  30. #ifndef CERES_INTERNAL_CUDA_KERNELS_UTILS_H_
  31. #define CERES_INTERNAL_CUDA_KERNELS_UTILS_H_
  32. namespace ceres {
  33. namespace internal {
  34. // Parallel execution on CUDA device requires splitting job into blocks of a
  35. // fixed size. We use block-size of kCudaBlockSize for all kernels that do not
  36. // require any specific block size. As the CUDA Toolkit documentation says,
  37. // "although arbitrary in this case, is a common choice". This is determined by
  38. // the warp size, max block size, and multiprocessor sizes of recent GPUs. For
  39. // complex kernels with significant register usage and unusual memory patterns,
  40. // the occupancy calculator API might provide better performance. See "Occupancy
  41. // Calculator" under the CUDA toolkit documentation.
  42. constexpr int kCudaBlockSize = 256;
  43. // Compute number of blocks of kCudaBlockSize that span over 1-d grid with
  44. // dimension size. Note that 1-d grid dimension is limited by 2^31-1 in CUDA,
  45. // thus a signed int is used as an argument.
  46. inline int NumBlocksInGrid(int size) {
  47. return (size + kCudaBlockSize - 1) / kCudaBlockSize;
  48. }
  49. } // namespace internal
  50. } // namespace ceres
  51. #endif // CERES_INTERNAL_CUDA_KERNELS_UTILS_H_