NDLI: Multicore Performance of Block Algebraic Iterative Reconstruction Methods

Content Provider	Society for Industrial and Applied Mathematics (SIAM)
Author	Srensen, Hans Henrik B. Hansen, Per Christian
Copyright Year	2014
Abstract	Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the algebraic reconstruction technique (ART) and the simultaneous iterative reconstruction techniques (SIRT), both of which rely on semiconvergence. Block versions of these methods, based on a partitioning of the linear system, are able to combine the fast semiconvergence of ART with the better multicore properties of SIRT. These block methods separate into two classes: those that, in each iteration, access the blocks in a sequential manner, and those that compute a result for each block in parallel and then combine these results before the next iteration. The goal of this work is to demonstrate which block methods are best suited for implementation on modern multicore computers. To compare the performance of the different block methods, we use a fixed relaxation parameter in each method, namely, the one that leads to the fastest semiconvergence. Computational results show that for multicore computers, the sequential approach is preferable.
Starting Page	C524
Ending Page	C546
Page Count	23
File Format	PDF
ISSN	10648275
DOI	10.1137/130920642
e-ISSN	10957197
Issue Number	5
Volume Number	36
Language	English
Publisher	Society for Industrial and Applied Mathematics
Publisher Date	2014-10-09
Access Restriction	Subscribed
Subject Keyword	block methods ART Inverse problems algebraic iterative reconstruction relaxation parameter Iterative methods for linear systems tomographic imaging SIRT semiconvergence
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Computational Mathematics

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