Abstract
The efficient use of multicore architectures for sparse matrix-vector multiplication (SpMV) is currently an open challenge. One algorithm which makes use of SpMV is the maximum likelihood expectation maximization (MLEM) algorithm. When using MLEM for positron emission tomography (PET) image reconstruction, one requires a particularly large matrix. We present a new storage scheme for this type of matrix which cuts the memory requirements by half, compared to the widely-used compressed sparse row format. For parallelization we combine the two partitioning techniques recursive bisection and striping. Our results show good load balancing and cache behavior. We also give speedup measurements on various modern multicore systems. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Küstner, T., Weidendorfer, J., Schirmer, J., Klug, T., Trinitis, C., & Ziegler, S. (2009). Parallel mlem on multicore architectures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5544 LNCS, pp. 491–500). https://doi.org/10.1007/978-3-642-01970-8_48
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