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https://github.com/ceres-solver/ceres-solver.git
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7d2e4152ec
1. Add CUDADenseQR & tests. CUDADenseQR uses the cuSolverDN LAPACK implementation of QR factorization. A key limitation, however, is that this solver does not perform singularity checking -- this is because cuSolverDN does not have a trtrs implementation; we instead use cuBLAS' trsv for backsubstitution. 2. All CPU -> GPU memory transfers are now async, and both CUDADenseQR and CUDADenseCholesky explicitly manage their own streams for async operations. 3. Simplified CUDADenseCholesky to only use the legacy 32-bit cuSolverDN API. Change-Id: I2a9b7b65469658ddfe33b5b2a3892c8744d6e437
110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2022 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
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#ifndef CERES_INTERNAL_CUDA_BUFFER_H_
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#define CERES_INTERNAL_CUDA_BUFFER_H_
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#include "ceres/internal/config.h"
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#ifndef CERES_NO_CUDA
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#include <vector>
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#include "cuda_runtime.h"
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#include "glog/logging.h"
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// An encapsulated buffer to maintain GPU memory, and handle transfers between
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// GPU and system memory. It is the responsibility of the user to ensure that
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// the appropriate GPU device is selected before each subroutine is called. This
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// is particularly important when using multiple GPU devices on different CPU
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// threads, since active Cuda devices are determined by the cuda runtime on a
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// per-thread basis. Note that unless otherwise specified, all methods use the
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// default stream, and are synchronous.
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template <typename T>
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class CudaBuffer {
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public:
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CudaBuffer() = default;
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CudaBuffer(const CudaBuffer&) = delete;
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CudaBuffer& operator=(const CudaBuffer&) = delete;
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~CudaBuffer() {
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if (data_ != nullptr) {
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CHECK_EQ(cudaFree(data_), cudaSuccess);
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}
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}
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// Grow the GPU memory buffer if needed to accommodate data of the specified
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// size
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void Reserve(const size_t size) {
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if (size > size_) {
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if (data_ != nullptr) {
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CHECK_EQ(cudaFree(data_), cudaSuccess);
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}
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CHECK_EQ(cudaMalloc(&data_, size * sizeof(T)), cudaSuccess);
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size_ = size;
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}
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}
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// Perform an asynchronous copy from CPU memory to GPU memory using the stream
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// provided.
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void CopyToGpuAsync(const T* data, const size_t size, cudaStream_t stream) {
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Reserve(size);
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CHECK_EQ(cudaMemcpyAsync(data_,
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data,
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size * sizeof(T),
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cudaMemcpyHostToDevice,
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stream),
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cudaSuccess);
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}
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// Copy data from the GPU to CPU memory. This is necessarily synchronous since
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// any potential GPU kernels that may be writing to the buffer must finish
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// before the transfer happens.
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void CopyToHost(T* data, const size_t size) {
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CHECK(data_ != nullptr);
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CHECK_EQ(cudaMemcpy(data, data_, size * sizeof(T), cudaMemcpyDeviceToHost),
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cudaSuccess);
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}
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void CopyToGpu(const std::vector<T>& data) {
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CopyToGpu(data.data(), data.size());
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}
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T* data() { return data_; }
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size_t size() const { return size_; }
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private:
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T* data_ = nullptr;
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size_t size_ = 0;
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};
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#endif // CERES_NO_CUDA
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#endif // CERES_INTERNAL_CUDA_BUFFER_H_
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