CN113191105A - Electrical simulation method based on distributed parallel operation method - Google Patents
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Abstract
The invention discloses an electrical simulation method based on a distributed parallel operation method, which specifically comprises the following steps: s1, updating Jacobian matrix elements and right-end items; s2, preprocessing the Jacobian matrix to reduce the condition number of the coefficient matrix; s3, solving a matrix equation by adopting a Krylov subspace method; s4, in each process, according to the task distributed by the main process, corresponding calculation is carried out; s5, collecting the data after the calculation of each process into a main process, and processing to obtain the current iteration solution vector; s6, convergence judgment is carried out, when the matrix is solved for convergence, the step S7 is carried out, otherwise, the step S3 is carried out, and solution vectors are obtained according to the current calculation; and S7, judging whether the current nonlinear iterative solution convergence condition is satisfied, and when the convergence condition is not satisfied, re-solving until the nonlinear solution convergence. The invention optimizes the design, and can obviously improve the solving efficiency of the simulation of a complex large system by fully utilizing hardware computing resources in the local area network.
Description
Technical Field
The invention relates to the technical field of computer virtual simulation, in particular to an electrical simulation method based on a distributed parallel operation method.
Background
The electrical simulation technology is widely applied to the design of complex electrical systems such as aviation, aerospace, ships, weaponry, rail transit and the like, and currently, a general electrical system simulator generally adopts a direct method based on LU triangular decomposition to perform system analog simulation. For a large sparse matrix, there is a great technical obstacle to realizing parallel operation of LU triangular decomposition, so that a current general solver generally adopts a single-node technology to perform electrical system simulation. With the development of multi-power and full-power technologies, independent power supply systems are more and more complex, and meanwhile, with the continuous progress of modeling technologies, the complexity of simulation models of electrical systems is higher and higher, and due to the fact that single-node hardware configuration is adopted, the requirements of higher and higher complex system simulation are difficult to meet due to low simulation efficiency.
Disclosure of Invention
The invention aims to provide an electrical simulation method based on a distributed parallel operation method, which aims to solve the problems in the background technology and improve the convergence performance and the solving efficiency of the simulation of a complex electrical system.
In order to achieve the purpose, the invention provides the following technical scheme:
an electrical simulation method based on a distributed parallel operation method specifically comprises the following steps:
s1, reading a netlist file by a solver during initialization, wherein the netlist file comprises detailed information of element types, element pin node connection information, element model parameters, simulation step lengths, convergence parameters and an integration method, and constructing a Jacobian initial matrix based on a node voltage method; when Newton-Raphson nonlinear iteration is carried out, updating the elements of the Jacobian matrix and the right-end term according to the current solution vector state at any iteration moment;
s2, preprocessing the Jacobian matrix to reduce the condition number of the coefficient matrix;
s3, solving a matrix equation by adopting a Krylov subspace method;
s4, in each process, according to the task distributed by the main process, corresponding calculation is carried out;
s5, collecting the data after the calculation of each process into a main process, and carrying out comprehensive processing on the data to obtain the current iteration solution vector;
s6, carrying out convergence judgment according to iteration results, entering the step S7 when the matrix solution is converged, otherwise returning to the step S3, updating the Jacobian matrix and the right-end item again according to the solution vector obtained by current calculation, and continuing to iteratively solve the current matrix until iteration is converged; when the iteration times exceed the maximum iteration times and still do not converge, warning information is given;
s7, comparing the state of the solution vector and the right-end term with a convergence criterion, judging whether the current nonlinear iteration solution convergence condition is satisfied, substituting the solution vector obtained by iteration into a Jacobian matrix and the right-end term when the convergence condition is not satisfied, updating equation coefficients and excitation elements again, and solving equations until the nonlinear solution convergence; and repeating the nonlinear iteration process until all the solving tasks are completed.
Preferably, the initial state of the solution vector in step S1 is obtained from historical solution data before entering the iteration, and the solution vector obtained for each iteration is obtained after entering the non-linear iteration.
Preferably, in step S1, in the newton-raphson nonlinear iterative calculation process, a step-changing algorithm is used.
Preferably, after the preprocessing algorithm is adopted in step S2, the right-end item is processed synchronously according to the selected preprocessor.
Preferably, in step S3, obtaining an expression of an equation iterative solution according to a Krylov subspace method, and performing solution operation by using a sparse matrix; when the solved object matrix is large, the calculation load is decomposed according to the hardware resources in the local area network and is distributed to each process of the local area network.
Compared with the prior art, the invention has the beneficial effects that: the invention provides an electrical simulation technology based on a distributed parallel operation method in order to improve the solving efficiency of the simulation of a complex electrical system. The method is based on a Krylov subspace iteration method to realize distributed parallel operation. Because the main computational load of the Krylov subspace iteration method is matrix vector multiplication, the sparse matrix can be fully utilized to further improve the computational efficiency. The distributed parallel computing realizes multi-process parallel computing based on MPI parallel environment. The invention optimizes the design, and can obviously improve the solving efficiency of the simulation of a complex large system by fully utilizing hardware computing resources in the local area network.
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Fig. 1 is a schematic flow chart of an electrical simulation method based on a distributed parallel operation method.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: an electrical simulation method based on a distributed parallel operation method specifically comprises the following steps:
an electrical simulation method based on a distributed parallel operation method specifically comprises the following steps:
s1, reading a netlist file by a solver during initialization, wherein the netlist file comprises detailed information of element types, element pin node connection information, element model parameters, simulation step lengths, convergence parameters and an integration method, and constructing a Jacobian initial matrix based on a node voltage method; when Newton-Raphson nonlinear iteration is carried out, updating the elements of the Jacobian matrix and the right-end term according to the current solution vector state at any iteration moment; in step S1, the initial state of the solution vector is obtained from the historical solution data before entering the iteration, and the solution vector is obtained for each iteration after entering the nonlinear iteration; in the Newton-Raphson nonlinear iterative computation process, a variable step length algorithm is adopted to improve the computation efficiency;
s2, preprocessing the Jacobian matrix to reduce the condition number of the coefficient matrix; after a preprocessing algorithm is adopted, the right-end item is synchronously processed according to the selected preprocessing son;
s3, solving a matrix equation by adopting a Krylov subspace method;
obviously, the numerical solution problem here can be converted to solve a general system of linear equations:
Ax＝b，
wherein the matrix A is formed by Rn×nAnd vector b ∈ RnGiven, x ∈ RnIs an unknown vector; setting a coefficient matrix A as a nonsingular large sparse matrix;
taking an initial vector x0∈RnSolving the system of equations is equivalent to solving
Ay＝r0，x＝x0+y，r0＝b-Ax0，
Constructing a Krylov subspace
Then one can be obtained
In a sense that it is the system of equations Ay r0Is best approximated by
y＝A-1r0≈yk＝h0r0+h1Ar0+hkAk-1r0，
The approximate solution of the corresponding original equation set Ax ═ b is xk＝x0+yk；
In the above calculation process, the matrix inversion operation is replaced by a matrix polynomial, and in the calculation process, the matrix A and the vector A are used as the basisk-1r0Multiplication recursion to obtain Akr0(ii) a Because the matrix A has very obvious sparse characteristics and the parallel calculation of the sparse matrix and the vector multiplication is easy to realize, the sparse matrix operation is adopted; by utilizing the characteristic of the sparse matrix, the storage space is reduced, and the calculation requirement is reduced;
the Sparse matrix Storage format adopted here is Row Compressed Storage (CSR); here, the CSR format is taken as an example to describe a storage method of the sparse matrix; setting a sparse matrix A, creating three arrays when adopting a CSR format, wherein one array is a floating-point array V, the other two arrays are integer arrays C and R, and the three arrays are respectively used for:
A) the V array dimension is the number of the non-zero elements of the matrix A, and the non-zero elements of the matrix A are stored in a row traversal mode from top to bottom and from left to right;
B) the dimension of the C array is the same as that of the V array and is used for storing the column index of the element in the V array;
C) the dimension of the R array is the row number of the matrix A, and the index of the first non-zero element in each row of the matrix A in V is stored;
in step S3, obtaining an expression of an equation iterative solution according to a Krylov subspace method, and performing solution operation by adopting a sparse matrix; when the solved object matrix is large, decomposing the calculation load according to the hardware resources in the local area network, and distributing the calculation load to each process of the local area network;
s4, in each process, according to the task distributed by the main process, corresponding calculation is carried out;
s5, collecting the data after the calculation of each process into a main process, and carrying out comprehensive processing on the data to obtain the current iteration solution vector;
s6, carrying out convergence judgment according to iteration results, entering the step S7 when the matrix solution is converged, otherwise returning to the step S3, updating the Jacobian matrix and the right-end item again according to the solution vector obtained by current calculation, and continuing to iteratively solve the current matrix until iteration is converged; when the iteration times exceed the maximum iteration times and still do not converge, warning information is given;
s7, comparing the state of the solution vector and the right-end term with a convergence criterion, judging whether the current nonlinear iteration solution convergence condition is satisfied, substituting the solution vector obtained by iteration into a Jacobian matrix and the right-end term when the convergence condition is not satisfied, updating equation coefficients and excitation elements again, and solving equations until the nonlinear solution convergence; and repeating the nonlinear iteration process until all the solving tasks are completed.
The invention provides an electrical simulation technology based on a distributed parallel operation method in order to improve the solving efficiency of the simulation of a complex electrical system. The method is based on a Krylov subspace iteration method to realize distributed parallel operation. By fully utilizing hardware computing resources in the local area network, the method can obviously improve the solving efficiency of the simulation of the complex large system. Because the main computational load of the Krylov subspace iteration method is matrix vector multiplication, the sparse matrix can be fully utilized to further improve the computational efficiency, and therefore in the method, the comprehensive simulation of the complex electrical system is realized by adopting a parallel computation algorithm based on the sparse matrix. The distributed parallel computing realizes multi-process parallel computing based on MPI parallel environment.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (5)
1. An electrical simulation method based on a distributed parallel operation method is characterized in that: the method specifically comprises the following steps:
s1, reading a netlist file by a solver during initialization, wherein the netlist file comprises detailed information of element types, element pin node connection information, element model parameters, simulation step lengths, convergence parameters and an integration method, and constructing a Jacobian initial matrix based on a node voltage method; when Newton-Raphson nonlinear iteration is carried out, updating the elements of the Jacobian matrix and the right-end term according to the current solution vector state at any iteration moment;
s2, preprocessing the Jacobian matrix to reduce the condition number of the coefficient matrix;
s3, solving a matrix equation by adopting a Krylov subspace method;
s4, in each process, according to the task distributed by the main process, corresponding calculation is carried out;
s5, collecting the data after the calculation of each process into a main process, and carrying out comprehensive processing on the data to obtain the current iteration solution vector;
s6, carrying out convergence judgment according to iteration results, entering the step S7 when the matrix solution is converged, otherwise returning to the step S3, updating the Jacobian matrix and the right-end item again according to the solution vector obtained by current calculation, and continuing to iteratively solve the current matrix until iteration is converged; when the iteration times exceed the maximum iteration times and still do not converge, warning information is given;
s7, comparing the state of the solution vector and the right-end term with a convergence criterion, judging whether the current nonlinear iteration solution convergence condition is satisfied, substituting the solution vector obtained by iteration into a Jacobian matrix and the right-end term when the convergence condition is not satisfied, updating equation coefficients and excitation elements again, and solving equations until the nonlinear solution convergence; and repeating the nonlinear iteration process until all the solving tasks are completed.
2. The electrical simulation method based on the distributed parallel operation method according to claim 1, wherein: the initial state of the solution vector in step S1 is obtained from the historical solution data before entering the iteration, and after entering the nonlinear iteration, the solution vector is obtained for each iteration.
3. The electrical simulation method based on the distributed parallel operation method according to claim 1, wherein: in the newton-raphson nonlinear iterative calculation process in step S1, a step-changing algorithm is employed.
4. The electrical simulation method based on the distributed parallel operation method according to claim 1, wherein: after the preprocessing algorithm is adopted in step S2, the right-end item is synchronously processed according to the selected preprocessor.
5. The electrical simulation method based on the distributed parallel operation method according to claim 1, wherein: in step S3, obtaining an expression of an equation iterative solution according to a Krylov subspace method, and performing solution operation by adopting a sparse matrix; when the solved object matrix is large, the calculation load is decomposed according to the hardware resources in the local area network and is distributed to each process of the local area network.
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| CN114329981A (en) * | 2021-12-29 | 2022-04-12 | 西安交通大学 | DGMRES double-iteration high-dimensional electromagnetic simulation method and system based on ADMM preconditions |
| CN115146573A (en) * | 2022-07-05 | 2022-10-04 | 北京华大九天科技股份有限公司 | Simulation method for small signal frequency response characteristic in analog circuit and related product |
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| CN114329981A (en) * | 2021-12-29 | 2022-04-12 | 西安交通大学 | DGMRES double-iteration high-dimensional electromagnetic simulation method and system based on ADMM preconditions |
| CN114329981B (en) * | 2021-12-29 | 2024-04-02 | 西安交通大学 | DGMRES double-iteration high-dimensional electromagnetic simulation method and system based on ADMM precondition |
| CN115146573A (en) * | 2022-07-05 | 2022-10-04 | 北京华大九天科技股份有限公司 | Simulation method for small signal frequency response characteristic in analog circuit and related product |
| CN115146573B (en) * | 2022-07-05 | 2024-08-20 | 北京华大九天科技股份有限公司 | Simulation method for small signal frequency response characteristics in analog circuit and related products |
| CN115718986A (en) * | 2022-10-31 | 2023-02-28 | 南方电网数字电网研究院有限公司 | Multi-core parallel time domain simulation method based on distributed memory architecture |
| CN115718986B (en) * | 2022-10-31 | 2023-12-12 | 南方电网数字电网研究院有限公司 | Multi-core parallel time domain simulation method based on distributed memory architecture |
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