// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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//   this list of conditions and the following disclaimer in the documentation
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// 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_