Computational Technology Resources - CCC

C. Topal, N. Muhtaroglu, G. Kiziltas, "Parallel Application of Multi-Freedom Constraints Using Master-Slave Method in Sparse Linear Systems", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighth International Conference on Parallel, Distributed, GPU and Cloud Computing for Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 12, Paper 2.2, 2025,

Keywords: linear equations, sparse matrices, multi-freedom constraints, parallel computation, MPI, PETSc.

Abstract

Multi-freedom constraints (MFCs) are commonly used in matrix formulations to enforce dependencies among multiple components, particularly in structural analysis where they are defined based on the degrees of freedom (DOFs) at nodes or computation points. A widely used approach for implementing MFCs is the master-slave elimination method, favored for its simplicity and its ability to reduce the number of unknowns. While straightforward to implement with full matrix storage, this approach can lead to increased memory usage. Conversely, applying it to sparse matrices presents added complexity. This paper introduces a scalable and reusable implementation of the master-slave method tailored for large-scale linear systems with multiple non-homogeneous constraints. The approach leverages parallel programming and distributed processing to efficiently handle computational demands. PETSc 3.23.1 is used as a tool for parallel computing due to its higher-level encapsulation of MPI operations and built-in sparse matrix representation methods. The algorithm's syntax is designed for efficient memory handling. Parallel MPI library enables load balancing across processors and threads.Benchmark results show that the proposed algorithm speeds up process solving linear systems with multi-freedom constraints.

download the full-text of this paper (PDF, 9 pages, 725 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 12 PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GPU AND CLOUD COMPUTING FOR ENGINEERING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 2.2 Parallel Application of Multi-Freedom Constraints Using Master-Slave Method in Sparse Linear Systems C. Topal¹, N. Muhtaroglu² and G. Kiziltas¹ ¹Mechatronics Engineering, Sabanci University, Istanbul, Turkey ²Computer Science, Ozyegin University, Istanbul, Turkey Full Bibliographic Reference for this paper C. Topal, N. Muhtaroglu, G. Kiziltas, "Parallel Application of Multi-Freedom Constraints Using Master-Slave Method in Sparse Linear Systems", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighth International Conference on Parallel, Distributed, GPU and Cloud Computing for Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 12, Paper 2.2, 2025, Keywords: linear equations, sparse matrices, multi-freedom constraints, parallel computation, MPI, PETSc. Abstract Multi-freedom constraints (MFCs) are commonly used in matrix formulations to enforce dependencies among multiple components, particularly in structural analysis where they are defined based on the degrees of freedom (DOFs) at nodes or computation points. A widely used approach for implementing MFCs is the master-slave elimination method, favored for its simplicity and its ability to reduce the number of unknowns. While straightforward to implement with full matrix storage, this approach can lead to increased memory usage. Conversely, applying it to sparse matrices presents added complexity. This paper introduces a scalable and reusable implementation of the master-slave method tailored for large-scale linear systems with multiple non-homogeneous constraints. The approach leverages parallel programming and distributed processing to efficiently handle computational demands. PETSc 3.23.1 is used as a tool for parallel computing due to its higher-level encapsulation of MPI operations and built-in sparse matrix representation methods. The algorithm's syntax is designed for efficient memory handling. Parallel MPI library enables load balancing across processors and threads.Benchmark results show that the proposed algorithm speeds up process solving linear systems with multi-freedom constraints. download the full-text of this paper (PDF, 9 pages, 725 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions