mirror of
https://github.com/ceres-solver/ceres-solver.git
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88e08cfe71
* Created a new class CUDADenseCholeskyMixedPrecision, which performs
Cholesky factorization and solving in single (fp32) precision, and
optionally performs iterative refinement.
* Added CUDA kernels for mixed-precision solve operations
* Added more detailed timing information to the FullReport about Schur
elimination, reduced system solves, and back-substitution.
Some test performance numbers follow.
All tests were performed on an Ubuntu 20.04 desktop with an
Intel Core i9-9940X CPU and Nvidia Quadro RTX 6000 GPU.
Tests were launched as:
./bin/bundle_adjuster --input (problem_file) \
--num_iterations 20
--num_threads 28
--linear_solver dense_schur
--dense_linear_algebra_library (cuda|lapack)
[--mixed_precision_solves]
==================================================
problem-21-11315-pre.txt
==================================================
--------------------------------------------------
Cuda Mixed Precision
--------------------------------------------------
Cost:
Initial 4.413239e+06
Final 3.037864e+04
Change 4.382861e+06
Linear solver 0.250703 (14)
├ Schur eliminate 0.234025 (14)
├ Reduced solve 0.006643 (14)
└ Backsubstitute 0.006598 (12)
--------------------------------------------------
Cuda
--------------------------------------------------
Cost:
Initial 4.413239e+06
Final 3.037864e+04
Change 4.382861e+06
Linear solver 0.257517 (12)
├ Schur eliminate 0.233518 (12)
├ Reduced solve 0.010621 (12)
└ Backsubstitute 0.007124 (12)
--------------------------------------------------
Lapack (OpenBLAS)
--------------------------------------------------
Cost:
Initial 4.413239e+06
Final 3.037864e+04
Change 4.382861e+06
Linear solver 0.332349 (12)
├ Schur eliminate 0.274748 (12)
├ Reduced solve 0.015966 (12)
└ Backsubstitute 0.034192 (12)
==================================================
problem-257-65132-pre.txt
==================================================
--------------------------------------------------
Cuda Mixed Precision
--------------------------------------------------
Cost:
Initial 2.456242e+07
Final 9.677593e+04
Change 2.446565e+07
Linear solver 1.332367 (20)
├ Schur eliminate 1.021365 (20)
├ Reduced solve 0.195472 (20)
└ Backsubstitute 0.075582 (20)
--------------------------------------------------
Cuda
--------------------------------------------------
Cost:
Initial 2.456242e+07
Final 9.677547e+04
Change 2.446565e+07
Linear solver 1.810176 (20)
├ Schur eliminate 1.012862 (20)
├ Reduced solve 0.678704 (20)
└ Backsubstitute 0.083925 (20)
--------------------------------------------------
Lapack (OpenBLAS)
--------------------------------------------------
Cost:
Initial 2.456242e+07
Final 9.677547e+04
Change 2.446565e+07
Linear solver 2.376273 (20)
├ Schur eliminate 0.987613 (20)
├ Reduced solve 1.043873 (20)
└ Backsubstitute 0.310402 (20)
==================================================
problem-744-543562-pre.txt
==================================================
--------------------------------------------------
Cuda Mixed Precision
--------------------------------------------------
Cost:
Initial 1.434881e+08
Final 1.546895e+06
Change 1.419412e+08
Linear solver 27.010088 (20)
├ Schur eliminate 24.362433 (20)
├ Reduced solve 1.428542 (20)
└ Backsubstitute 0.814266 (20)
--------------------------------------------------
Cuda
--------------------------------------------------
Cost:
Initial 1.434881e+08
Final 1.546895e+06
Change 1.419412e+08
Linear solver 32.342513 (20)
├ Schur eliminate 24.638819 (20)
├ Reduced solve 6.492090 (20)
└ Backsubstitute 0.802184 (20)
--------------------------------------------------
Lapack (OpenBLAS)
--------------------------------------------------
Cost:
Initial 1.434881e+08
Final 1.546895e+06
Change 1.419412e+08
Linear solver 34.152224 (20)
├ Schur eliminate 24.183723 (20)
├ Reduced solve 8.784413 (20)
└ Backsubstitute 0.795044 (20)
Change-Id: I178887e776d8f4a1e8abb99bbc205bf8c278bf79
116 lines
4.3 KiB
C++
116 lines
4.3 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(
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data_, data, size * sizeof(T), cudaMemcpyHostToDevice, stream),
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cudaSuccess);
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}
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// Perform an asynchronous copy from GPU memory using the stream provided.
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void CopyFromGpuAsync(const T* data, const size_t size, cudaStream_t stream) {
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Reserve(size);
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CHECK_EQ(cudaMemcpyAsync(
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data_, data, size * sizeof(T), cudaMemcpyDeviceToDevice, 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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const T* data() const { 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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