text: // 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_BUFFER_H_
  #define CERES_INTERNAL_CUDA_BUFFER_H_
  
  #include "ceres/internal/config.h"
  
  #ifndef CERES_NO_CUDA
  
  #include <vector>
  
  #include "cuda_runtime.h"
  #include "glog/logging.h"
  
  // An encapsulated buffer to maintain GPU memory, and handle transfers between
  // GPU and system memory. It is the responsibility of the user to ensure that
  // the appropriate GPU device is selected before each subroutine is called. This
  // is particularly important when using multiple GPU devices on different CPU
  // threads, since active Cuda devices are determined by the cuda runtime on a
  // per-thread basis.
  template <typename T>
  class CudaBuffer {
   public:
    CudaBuffer() = default;
    CudaBuffer(const CudaBuffer&) = delete;
    CudaBuffer& operator=(const CudaBuffer&) = delete;
  
    ~CudaBuffer() {
      if (data_ != nullptr) {
        CHECK_EQ(cudaFree(data_), cudaSuccess);
      }
    }
  
    void Reserve(const size_t size) {
      if (size > size_) {
        if (data_ != nullptr) {
          CHECK_EQ(cudaFree(data_), cudaSuccess);
        }
        CHECK_EQ(cudaMalloc(&data_, size * sizeof(T)), cudaSuccess);
        size_ = size;
      }
    }
  
    void CopyToGpu(const T* data, const size_t size) {
      Reserve(size);
      CHECK_EQ(cudaMemcpy(data_, data, size * sizeof(T), cudaMemcpyHostToDevice),
               cudaSuccess);
    }
  
    void CopyToHost(T* data, const size_t size) {
      CHECK(data_ != nullptr);
      CHECK_EQ(cudaMemcpy(data, data_, size * sizeof(T), cudaMemcpyDeviceToHost),
               cudaSuccess);
    }
  
    void CopyToGpu(const std::vector<T>& data) {
      CopyToGpu(data.data(), data.size());
    }
  
    T* data() { return data_; }
    size_t size() const { return size_; }
  
   private:
    T* data_ = nullptr;
    size_t size_ = 0;
  };
  
  #endif  // CERES_NO_CUDA
  
  #endif  // CERES_INTERNAL_CUDA_BUFFER_H_