mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Dmitriy Korchemkin
bdee4d6172
Instead of pre-computing pemutation from block-sparse to CRS order, index of value in CRS matrix is computed in the process of updating values using block-sparse structure. When it is possible to update values via a simple host-to-device copy, block-sparse structure on GPU is discarded after computing CRS structure. Computing index is significantly slower than using pre-computed permutation, but is still hidden by host-to-device transfer. On problems from BAL dataset this results into reduction of extra gpu memory consumption from 33% (permutation stored as 32-bit indices) to ~10% for storing block-sparse structure. Benchmark results: ======================= CUDA Device Properties ====================== Cuda version : 11.8 Device ID : 0 Device name : NVIDIA GeForce RTX 2080 Ti Total GPU memory : 11012 MiB GPU memory available : 10852 MiB Compute capability : 7.5 Warp size : 32 Max threads per block: 1024 Max threads per dim : 1024 1024 64 Max grid size : 2147483647 65535 65535 Multiprocessor count : 68 ==================================================================== Running ./bin/evaluation_benchmark Run on (112 X 3200 MHz CPU s) CPU Caches: L1 Data 32 KiB (x56) L1 Instruction 32 KiB (x56) L2 Unified 1024 KiB (x56) L3 Unified 39424 KiB (x2) Load Average: 24.58, 11.75, 8.52 ----------------------------------------------------------------------- Benchmark Time ----------------------------------------------------------------------- Using on-the-fly computation of CRS index corresponding to block-sparse index: JacobianToCRS<g/final/problem-4585-1324582-pre.txt> 1607 ms JacobianToCRSView<g/final/problem-4585-1324582-pre.txt> 564 ms JacobianToCRSMatrix<g/final/problem-4585-1324582-pre.txt> 2226 ms JacobianToCRSViewUpdate<g/final/problem-4585-1324582-pre.txt> 228 ms JacobianToCRSMatrixUpdate<g/final/problem-4585-1324582-pre.txt> 400 ms Using precomputed permutation: JacobianToCRS</final/problem-4585-1324582-pre.txt> 1656 ms JacobianToCRSView</final/problem-4585-1324582-pre.txt> 553 ms JacobianToCRSMatrix</final/problem-4585-1324582-pre.txt> 2255 ms JacobianToCRSViewUpdate</final/problem-4585-1324582-pre.txt> 228 ms JacobianToCRSMatrixUpdate</final/problem-4585-1324582-pre.txt> 406 ms Performance of JacobianToCRSViewUpdate is still limited by host-to-device transfer, and JacobianToCRSView is faster than computing CRS structure on CPU. Change-Id: Ifb6910fb01ae6071400d36c277846fadc5857964
84 lines
3.3 KiB
C++
84 lines
3.3 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_KERNELS_VECTOR_OPS_H_
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#define CERES_INTERNAL_CUDA_KERNELS_VECTOR_OPS_H_
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#include "ceres/internal/config.h"
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#ifndef CERES_NO_CUDA
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#include "cuda_runtime.h"
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namespace ceres {
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namespace internal {
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class Block;
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class Cell;
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// Convert an array of double (FP64) values to float (FP32). Both arrays must
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// already be on GPU memory.
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void CudaFP64ToFP32(const double* input,
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float* output,
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const int size,
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cudaStream_t stream);
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// Convert an array of float (FP32) values to double (FP64). Both arrays must
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// already be on GPU memory.
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void CudaFP32ToFP64(const float* input,
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double* output,
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const int size,
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cudaStream_t stream);
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// Set all elements of the array to the FP32 value 0. The array must be in GPU
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// memory.
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void CudaSetZeroFP32(float* output, const int size, cudaStream_t stream);
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// Set all elements of the array to the FP64 value 0. The array must be in GPU
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// memory.
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void CudaSetZeroFP64(double* output, const int size, cudaStream_t stream);
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// Compute x = x + double(y). Input array is float (FP32), output array is
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// double (FP64). Both arrays must already be on GPU memory.
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void CudaDsxpy(double* x, float* y, const int size, cudaStream_t stream);
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// Compute y[i] = y[i] + d[i]^2 x[i]. All arrays must already be on GPU memory.
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void CudaDtDxpy(double* y,
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const double* D,
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const double* x,
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const int size,
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cudaStream_t stream);
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} // namespace internal
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} // namespace ceres
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#endif // CERES_NO_CUDA
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#endif // CERES_INTERNAL_CUDA_KERNELS_VECTOR_OPS_H_
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