Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
new file mode 100644
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+++ b/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_GEMM_KERNEL_H
+#define EIGEN_SPARSELU_GEMM_KERNEL_H
+
+namespace Eigen {
+
+namespace internal {
+
+
+/** \internal
+ * A general matrix-matrix product kernel optimized for the SparseLU factorization.
+ * - A, B, and C must be column major
+ * - lda and ldc must be multiples of the respective packet size
+ * - C must have the same alignment as A
+ */
+template<typename Scalar,typename Index>
+EIGEN_DONT_INLINE
+void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const Scalar* B, Index ldb, Scalar* C, Index ldc)
+{
+ using namespace Eigen::internal;
+
+ typedef typename packet_traits<Scalar>::type Packet;
+ enum {
+ NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+ PacketSize = packet_traits<Scalar>::size,
+ PM = 8, // peeling in M
+ RN = 2, // register blocking
+ RK = NumberOfRegisters>=16 ? 4 : 2, // register blocking
+ BM = 4096/sizeof(Scalar), // number of rows of A-C per chunk
+ SM = PM*PacketSize // step along M
+ };
+ Index d_end = (d/RK)*RK; // number of columns of A (rows of B) suitable for full register blocking
+ Index n_end = (n/RN)*RN; // number of columns of B-C suitable for processing RN columns at once
+ Index i0 = internal::first_aligned(A,m);
+
+ eigen_internal_assert(((lda%PacketSize)==0) && ((ldc%PacketSize)==0) && (i0==internal::first_aligned(C,m)));
+
+ // handle the non aligned rows of A and C without any optimization:
+ for(Index i=0; i<i0; ++i)
+ {
+ for(Index j=0; j<n; ++j)
+ {
+ Scalar c = C[i+j*ldc];
+ for(Index k=0; k<d; ++k)
+ c += B[k+j*ldb] * A[i+k*lda];
+ C[i+j*ldc] = c;
+ }
+ }
+ // process the remaining rows per chunk of BM rows
+ for(Index ib=i0; ib<m; ib+=BM)
+ {
+ Index actual_b = std::min<Index>(BM, m-ib); // actual number of rows
+ Index actual_b_end1 = (actual_b/SM)*SM; // actual number of rows suitable for peeling
+ Index actual_b_end2 = (actual_b/PacketSize)*PacketSize; // actual number of rows suitable for vectorization
+
+ // Let's process two columns of B-C at once
+ for(Index j=0; j<n_end; j+=RN)
+ {
+ const Scalar* Bc0 = B+(j+0)*ldb;
+ const Scalar* Bc1 = B+(j+1)*ldb;
+
+ for(Index k=0; k<d_end; k+=RK)
+ {
+
+ // load and expand a RN x RK block of B
+ Packet b00, b10, b20, b30, b01, b11, b21, b31;
+ b00 = pset1<Packet>(Bc0[0]);
+ b10 = pset1<Packet>(Bc0[1]);
+ if(RK==4) b20 = pset1<Packet>(Bc0[2]);
+ if(RK==4) b30 = pset1<Packet>(Bc0[3]);
+ b01 = pset1<Packet>(Bc1[0]);
+ b11 = pset1<Packet>(Bc1[1]);
+ if(RK==4) b21 = pset1<Packet>(Bc1[2]);
+ if(RK==4) b31 = pset1<Packet>(Bc1[3]);
+
+ Packet a0, a1, a2, a3, c0, c1, t0, t1;
+
+ const Scalar* A0 = A+ib+(k+0)*lda;
+ const Scalar* A1 = A+ib+(k+1)*lda;
+ const Scalar* A2 = A+ib+(k+2)*lda;
+ const Scalar* A3 = A+ib+(k+3)*lda;
+
+ Scalar* C0 = C+ib+(j+0)*ldc;
+ Scalar* C1 = C+ib+(j+1)*ldc;
+
+ a0 = pload<Packet>(A0);
+ a1 = pload<Packet>(A1);
+ if(RK==4)
+ {
+ a2 = pload<Packet>(A2);
+ a3 = pload<Packet>(A3);
+ }
+ else
+ {
+ // workaround "may be used uninitialized in this function" warning
+ a2 = a3 = a0;
+ }
+
+#define KMADD(c, a, b, tmp) {tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);}
+#define WORK(I) \
+ c0 = pload<Packet>(C0+i+(I)*PacketSize); \
+ c1 = pload<Packet>(C1+i+(I)*PacketSize); \
+ KMADD(c0, a0, b00, t0) \
+ KMADD(c1, a0, b01, t1) \
+ a0 = pload<Packet>(A0+i+(I+1)*PacketSize); \
+ KMADD(c0, a1, b10, t0) \
+ KMADD(c1, a1, b11, t1) \
+ a1 = pload<Packet>(A1+i+(I+1)*PacketSize); \
+ if(RK==4) KMADD(c0, a2, b20, t0) \
+ if(RK==4) KMADD(c1, a2, b21, t1) \
+ if(RK==4) a2 = pload<Packet>(A2+i+(I+1)*PacketSize); \
+ if(RK==4) KMADD(c0, a3, b30, t0) \
+ if(RK==4) KMADD(c1, a3, b31, t1) \
+ if(RK==4) a3 = pload<Packet>(A3+i+(I+1)*PacketSize); \
+ pstore(C0+i+(I)*PacketSize, c0); \
+ pstore(C1+i+(I)*PacketSize, c1)
+
+ // process rows of A' - C' with aggressive vectorization and peeling
+ for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
+ {
+ EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL1");
+ prefetch((A0+i+(5)*PacketSize));
+ prefetch((A1+i+(5)*PacketSize));
+ if(RK==4) prefetch((A2+i+(5)*PacketSize));
+ if(RK==4) prefetch((A3+i+(5)*PacketSize));
+ WORK(0);
+ WORK(1);
+ WORK(2);
+ WORK(3);
+ WORK(4);
+ WORK(5);
+ WORK(6);
+ WORK(7);
+ }
+ // process the remaining rows with vectorization only
+ for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
+ {
+ WORK(0);
+ }
+#undef WORK
+ // process the remaining rows without vectorization
+ for(Index i=actual_b_end2; i<actual_b; ++i)
+ {
+ if(RK==4)
+ {
+ C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
+ C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]+A2[i]*Bc1[2]+A3[i]*Bc1[3];
+ }
+ else
+ {
+ C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
+ C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1];
+ }
+ }
+
+ Bc0 += RK;
+ Bc1 += RK;
+ } // peeled loop on k
+ } // peeled loop on the columns j
+ // process the last column (we now perform a matrux-vector product)
+ if((n-n_end)>0)
+ {
+ const Scalar* Bc0 = B+(n-1)*ldb;
+
+ for(Index k=0; k<d_end; k+=RK)
+ {
+
+ // load and expand a 1 x RK block of B
+ Packet b00, b10, b20, b30;
+ b00 = pset1<Packet>(Bc0[0]);
+ b10 = pset1<Packet>(Bc0[1]);
+ if(RK==4) b20 = pset1<Packet>(Bc0[2]);
+ if(RK==4) b30 = pset1<Packet>(Bc0[3]);
+
+ Packet a0, a1, a2, a3, c0, t0/*, t1*/;
+
+ const Scalar* A0 = A+ib+(k+0)*lda;
+ const Scalar* A1 = A+ib+(k+1)*lda;
+ const Scalar* A2 = A+ib+(k+2)*lda;
+ const Scalar* A3 = A+ib+(k+3)*lda;
+
+ Scalar* C0 = C+ib+(n_end)*ldc;
+
+ a0 = pload<Packet>(A0);
+ a1 = pload<Packet>(A1);
+ if(RK==4)
+ {
+ a2 = pload<Packet>(A2);
+ a3 = pload<Packet>(A3);
+ }
+ else
+ {
+ // workaround "may be used uninitialized in this function" warning
+ a2 = a3 = a0;
+ }
+
+#define WORK(I) \
+ c0 = pload<Packet>(C0+i+(I)*PacketSize); \
+ KMADD(c0, a0, b00, t0) \
+ a0 = pload<Packet>(A0+i+(I+1)*PacketSize); \
+ KMADD(c0, a1, b10, t0) \
+ a1 = pload<Packet>(A1+i+(I+1)*PacketSize); \
+ if(RK==4) KMADD(c0, a2, b20, t0) \
+ if(RK==4) a2 = pload<Packet>(A2+i+(I+1)*PacketSize); \
+ if(RK==4) KMADD(c0, a3, b30, t0) \
+ if(RK==4) a3 = pload<Packet>(A3+i+(I+1)*PacketSize); \
+ pstore(C0+i+(I)*PacketSize, c0);
+
+ // agressive vectorization and peeling
+ for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
+ {
+ EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2");
+ WORK(0);
+ WORK(1);
+ WORK(2);
+ WORK(3);
+ WORK(4);
+ WORK(5);
+ WORK(6);
+ WORK(7);
+ }
+ // vectorization only
+ for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
+ {
+ WORK(0);
+ }
+ // remaining scalars
+ for(Index i=actual_b_end2; i<actual_b; ++i)
+ {
+ if(RK==4)
+ C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
+ else
+ C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
+ }
+
+ Bc0 += RK;
+#undef WORK
+ }
+ }
+
+ // process the last columns of A, corresponding to the last rows of B
+ Index rd = d-d_end;
+ if(rd>0)
+ {
+ for(Index j=0; j<n; ++j)
+ {
+ enum {
+ Alignment = PacketSize>1 ? Aligned : 0
+ };
+ typedef Map<Matrix<Scalar,Dynamic,1>, Alignment > MapVector;
+ typedef Map<const Matrix<Scalar,Dynamic,1>, Alignment > ConstMapVector;
+ if(rd==1) MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b);
+
+ else if(rd==2) MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
+ + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b);
+
+ else MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
+ + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b)
+ + B[2+d_end+j*ldb] * ConstMapVector(A+(d_end+2)*lda+ib, actual_b);
+ }
+ }
+
+ } // blocking on the rows of A and C
+}
+#undef KMADD
+
+} // namespace internal
+
+} // namespace Eigen
+
+#endif // EIGEN_SPARSELU_GEMM_KERNEL_H