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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_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. Note that unless otherwise specified, all methods use the
// default stream, and are synchronous.
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);
}
}
// Grow the GPU memory buffer if needed to accommodate data of the specified
// size
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;
}
}
// Perform an asynchronous copy from CPU memory to GPU memory using the stream
// provided.
void CopyToGpuAsync(const T* data, const size_t size, cudaStream_t stream) {
Reserve(size);
CHECK_EQ(cudaMemcpyAsync(
data_, data, size * sizeof(T), cudaMemcpyHostToDevice, stream),
cudaSuccess);
}
// Copy data from the GPU to CPU memory. This is necessarily synchronous since
// any potential GPU kernels that may be writing to the buffer must finish
// before the transfer happens.
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_
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