2015SIAM Journal on Scientific ComputingRequires access

Communication Avoiding ILU0 Preconditioner

Laura Grigori, Sophie Moufawad

Open publisher page 31 citations

Abstract

In this paper we present a communication avoiding ILU0 preconditioner for solving large linear systems of equations by using iterative Krylov subspace methods. Recent research has focused on communication avoiding Krylov subspace methods based on so-called s-step methods. However, there are not many communication avoiding preconditioners yet, and this represents a serious limitation of these methods. Our preconditioner allows us to perform $s$ iterations of the iterative method with no communication, through ghosting some of the input data and performing redundant computation. To avoid communication, an alternating reordering algorithm is introduced for structured and well partitioned unstructured matrices, which requires the input matrix to be ordered by using a graph partitioning technique such as k-way or nested dissection. We show that the reordering does not affect the convergence rate of the ILU0 preconditioned system as compared to k-way or nested dissection ordering, while it reduces data movement and is expected to reduce the time needed to solve a linear system. In addition to communication avoiding Krylov subspace methods, our preconditioner can be used with classical methods such as GMRES to reduce communication.

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What this paper is about

In this paper we present a communication avoiding ILU0 preconditioner for solving large linear systems of equations by using iterative Krylov subspace methods. Recent research has focused on communication avoiding Krylov subspace methods based on so-called s-step methods. However, there are not many communication avoiding preconditioners yet, and this represents a serious limitation of these methods. Our preconditioner allows us to perform $s$ iterations of the iterative method with no communication, through ghosting some of the input data and performing redundant computation. To avoid communication, an alternating reordering algorithm is introduced for structured and well partitioned unstructured matrices, which requires the input matrix to be ordered by using a graph partitioning technique such as k-way or nested dissection. We show that the reordering does not affect the convergence rate of the ILU0 preconditioned system as compared to k-way or nested dissection ordering, while it reduces data movement and is expected to reduce the time needed to solve a linear system. In addition to communication avoiding Krylov subspace methods, our preconditioner can be used with classical methods such as GMRES to reduce communication.

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Available abstract

In this paper we present a communication avoiding ILU0 preconditioner for solving large linear systems of equations by using iterative Krylov subspace methods. Recent research has focused on communication avoiding Krylov subspace methods based on so-called s-step methods. However, there are not many communication avoiding preconditioners yet, and this represents a serious limitation of these methods. Our preconditioner allows us to perform $s$ iterations of the iterative method with no communication, through ghosting some of the input data and performing redundant computation. To avoid communication, an alternating reordering algorithm is introduced for structured and well partitioned unstructured matrices, which requires the input matrix to be ordered by using a graph partitioning technique such as k-way or nested dissection. We show that the reordering does not affect the convergence rate of the ILU0 preconditioned system as compared to k-way or nested dissection ordering, while it reduces data movement and is expected to reduce the time needed to solve a linear system. In addition to communication avoiding Krylov subspace methods, our preconditioner can be used with classical methods such as GMRES to reduce communication.

Key concepts: Preconditioner, Krylov subspace, Generalized minimal residual method, Computer science, Linear system, Iterative method, Subspace topology, Rate of convergence

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