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Add GPU sparse direct solvers (Cholesky, LDL^T, LU) via cuDSS, 1D/2D FFT via cuFFT with plan caching, and sparse matrix-vector/matrix multiply (SpMV/SpMM) via cuSPARSE. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
60 lines
1.8 KiB
C++
60 lines
1.8 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// GPU sparse LU factorization via cuDSS.
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//
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// For general (non-symmetric) sparse matrices. Uses pivoting.
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// Same three-phase workflow as GpuSparseLLT.
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//
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// Usage:
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// GpuSparseLU<double> lu(A); // analyze + factorize
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// VectorXd x = lu.solve(b); // solve
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#ifndef EIGEN_GPU_SPARSE_LU_H
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#define EIGEN_GPU_SPARSE_LU_H
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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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#include "./GpuSparseSolverBase.h"
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namespace Eigen {
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/** GPU sparse LU factorization (general matrices).
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*
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* Wraps cuDSS with CUDSS_MTYPE_GENERAL and CUDSS_MVIEW_FULL.
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* Accepts ColMajor SparseMatrix (CSC); internally converts to RowMajor
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* CSR since cuDSS requires CSR input.
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*
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* \tparam Scalar_ float, double, complex<float>, or complex<double>
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*/
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template <typename Scalar_>
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class GpuSparseLU : public internal::GpuSparseSolverBase<Scalar_, GpuSparseLU<Scalar_>> {
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using Base = internal::GpuSparseSolverBase<Scalar_, GpuSparseLU>;
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friend Base;
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public:
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using Scalar = Scalar_;
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GpuSparseLU() = default;
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template <typename InputType>
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explicit GpuSparseLU(const SparseMatrixBase<InputType>& A) {
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this->compute(A);
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}
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static constexpr bool needs_csr_conversion() { return true; }
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static constexpr cudssMatrixType_t cudss_matrix_type() { return CUDSS_MTYPE_GENERAL; }
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static constexpr cudssMatrixViewType_t cudss_matrix_view() { return CUDSS_MVIEW_FULL; }
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};
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} // namespace Eigen
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#endif // EIGEN_GPU_SPARSE_LU_H
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