// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2023 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: yangfan34@lenovo.com (Lenovo Research Device+ Lab - Shanghai) // // Optimization for simple blas functions used in the Schur Eliminator. // These are fairly basic implementations which already yield a significant // speedup in the eliminator performance. #ifndef CERES_INTERNAL_SMALL_BLAS_GENERIC_H_ #define CERES_INTERNAL_SMALL_BLAS_GENERIC_H_ namespace ceres::internal { // The following macros are used to share code #define CERES_GEMM_OPT_NAIVE_HEADER \ double cvec4[4] = {0.0, 0.0, 0.0, 0.0}; \ const double* pa = a; \ const double* pb = b; \ const int span = 4; \ int col_r = col_a & (span - 1); \ int col_m = col_a - col_r; #define CERES_GEMM_OPT_STORE_MAT1X4 \ if (kOperation > 0) { \ c[0] += cvec4[0]; \ c[1] += cvec4[1]; \ c[2] += cvec4[2]; \ c[3] += cvec4[3]; \ } else if (kOperation < 0) { \ c[0] -= cvec4[0]; \ c[1] -= cvec4[1]; \ c[2] -= cvec4[2]; \ c[3] -= cvec4[3]; \ } else { \ c[0] = cvec4[0]; \ c[1] = cvec4[1]; \ c[2] = cvec4[2]; \ c[3] = cvec4[3]; \ } \ c += 4; // Matrix-Matrix Multiplication // Figure out 1x4 of Matrix C in one batch // // c op a * B; // where op can be +=, -=, or =, indicated by kOperation. // // Matrix C Matrix A Matrix B // // C0, C1, C2, C3 op A0, A1, A2, A3, ... * B0, B1, B2, B3 // B4, B5, B6, B7 // B8, B9, Ba, Bb // Bc, Bd, Be, Bf // . , . , . , . // . , . , . , . // . , . , . , . // // unroll for loops // utilize the data resided in cache // NOTE: col_a means the columns of A static inline void MMM_mat1x4(const int col_a, const double* a, const double* b, const int col_stride_b, double* c, const int kOperation) { CERES_GEMM_OPT_NAIVE_HEADER double av = 0.0; int bi = 0; #define CERES_GEMM_OPT_MMM_MAT1X4_MUL \ av = pa[k]; \ pb = b + bi; \ cvec4[0] += av * pb[0]; \ cvec4[1] += av * pb[1]; \ cvec4[2] += av * pb[2]; \ cvec4[3] += av * pb[3]; \ pb += 4; \ bi += col_stride_b; \ k++; for (int k = 0; k < col_m;) { CERES_GEMM_OPT_MMM_MAT1X4_MUL CERES_GEMM_OPT_MMM_MAT1X4_MUL CERES_GEMM_OPT_MMM_MAT1X4_MUL CERES_GEMM_OPT_MMM_MAT1X4_MUL } for (int k = col_m; k < col_a;) { CERES_GEMM_OPT_MMM_MAT1X4_MUL } CERES_GEMM_OPT_STORE_MAT1X4 #undef CERES_GEMM_OPT_MMM_MAT1X4_MUL } // Matrix Transpose-Matrix multiplication // Figure out 1x4 of Matrix C in one batch // // c op a' * B; // where op can be +=, -=, or = indicated by kOperation. // // Matrix A // // A0 // A1 // A2 // A3 // . // . // . // // Matrix C Matrix A' Matrix B // // C0, C1, C2, C3 op A0, A1, A2, A3, ... * B0, B1, B2, B3 // B4, B5, B6, B7 // B8, B9, Ba, Bb // Bc, Bd, Be, Bf // . , . , . , . // . , . , . , . // . , . , . , . // // unroll for loops // utilize the data resided in cache // NOTE: col_a means the columns of A' static inline void MTM_mat1x4(const int col_a, const double* a, const int col_stride_a, const double* b, const int col_stride_b, double* c, const int kOperation) { CERES_GEMM_OPT_NAIVE_HEADER double av = 0.0; int ai = 0; int bi = 0; #define CERES_GEMM_OPT_MTM_MAT1X4_MUL \ av = pa[ai]; \ pb = b + bi; \ cvec4[0] += av * pb[0]; \ cvec4[1] += av * pb[1]; \ cvec4[2] += av * pb[2]; \ cvec4[3] += av * pb[3]; \ pb += 4; \ ai += col_stride_a; \ bi += col_stride_b; for (int k = 0; k < col_m; k += span) { CERES_GEMM_OPT_MTM_MAT1X4_MUL CERES_GEMM_OPT_MTM_MAT1X4_MUL CERES_GEMM_OPT_MTM_MAT1X4_MUL CERES_GEMM_OPT_MTM_MAT1X4_MUL } for (int k = col_m; k < col_a; k++) { CERES_GEMM_OPT_MTM_MAT1X4_MUL } CERES_GEMM_OPT_STORE_MAT1X4 #undef CERES_GEMM_OPT_MTM_MAT1X4_MUL } // Matrix-Vector Multiplication // Figure out 4x1 of vector c in one batch // // c op A * b; // where op can be +=, -=, or =, indicated by kOperation. // // Vector c Matrix A Vector b // // C0 op A0, A1, A2, A3, ... * B0 // C1 A4, A5, A6, A7, ... B1 // C2 A8, A9, Aa, Ab, ... B2 // C3 Ac, Ad, Ae, Af, ... B3 // . // . // . // // unroll for loops // utilize the data resided in cache // NOTE: col_a means the columns of A static inline void MVM_mat4x1(const int col_a, const double* a, const int col_stride_a, const double* b, double* c, const int kOperation) { CERES_GEMM_OPT_NAIVE_HEADER double bv = 0.0; // clang-format off #define CERES_GEMM_OPT_MVM_MAT4X1_MUL \ bv = *pb; \ cvec4[0] += *(pa ) * bv; \ cvec4[1] += *(pa + col_stride_a ) * bv; \ cvec4[2] += *(pa + col_stride_a * 2) * bv; \ cvec4[3] += *(pa + col_stride_a * 3) * bv; \ pa++; \ pb++; // clang-format on for (int k = 0; k < col_m; k += span) { CERES_GEMM_OPT_MVM_MAT4X1_MUL CERES_GEMM_OPT_MVM_MAT4X1_MUL CERES_GEMM_OPT_MVM_MAT4X1_MUL CERES_GEMM_OPT_MVM_MAT4X1_MUL } for (int k = col_m; k < col_a; k++) { CERES_GEMM_OPT_MVM_MAT4X1_MUL } CERES_GEMM_OPT_STORE_MAT1X4 #undef CERES_GEMM_OPT_MVM_MAT4X1_MUL } // Matrix Transpose-Vector multiplication // Figure out 4x1 of vector c in one batch // // c op A' * b; // where op can be +=, -=, or =, indicated by kOperation. // // Matrix A // // A0, A4, A8, Ac // A1, A5, A9, Ad // A2, A6, Aa, Ae // A3, A7, Ab, Af // . , . , . , . // . , . , . , . // . , . , . , . // // Vector c Matrix A' Vector b // // C0 op A0, A1, A2, A3, ... * B0 // C1 A4, A5, A6, A7, ... B1 // C2 A8, A9, Aa, Ab, ... B2 // C3 Ac, Ad, Ae, Af, ... B3 // . // . // . // // unroll for loops // utilize the data resided in cache // NOTE: col_a means the columns of A' static inline void MTV_mat4x1(const int col_a, const double* a, const int col_stride_a, const double* b, double* c, const int kOperation) { CERES_GEMM_OPT_NAIVE_HEADER double bv = 0.0; #define CERES_GEMM_OPT_MTV_MAT4X1_MUL \ bv = *pb; \ cvec4[0] += pa[0] * bv; \ cvec4[1] += pa[1] * bv; \ cvec4[2] += pa[2] * bv; \ cvec4[3] += pa[3] * bv; \ pa += col_stride_a; \ pb++; for (int k = 0; k < col_m; k += span) { CERES_GEMM_OPT_MTV_MAT4X1_MUL CERES_GEMM_OPT_MTV_MAT4X1_MUL CERES_GEMM_OPT_MTV_MAT4X1_MUL CERES_GEMM_OPT_MTV_MAT4X1_MUL } for (int k = col_m; k < col_a; k++) { CERES_GEMM_OPT_MTV_MAT4X1_MUL } CERES_GEMM_OPT_STORE_MAT1X4 #undef CERES_GEMM_OPT_MTV_MAT4X1_MUL } #undef CERES_GEMM_OPT_NAIVE_HEADER #undef CERES_GEMM_OPT_STORE_MAT1X4 } // namespace ceres::internal #endif // CERES_INTERNAL_SMALL_BLAS_GENERIC_H_