Files
ceres-solver/internal/ceres/cuda_buffer.h
T
Joydeep Biswas 88e08cfe71 Mixed-precision Iterative Refinement Cholesky With CUDA
* 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
2022-07-13 06:55:31 -05:00

116 lines
4.3 KiB
C++

// 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);
}
// Perform an asynchronous copy from GPU memory using the stream provided.
void CopyFromGpuAsync(const T* data, const size_t size, cudaStream_t stream) {
Reserve(size);
CHECK_EQ(cudaMemcpyAsync(
data_, data, size * sizeof(T), cudaMemcpyDeviceToDevice, 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_; }
const T* data() const { 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_