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DTSTAMP:20211207T054809Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211118T110000
DTEND;TZID=America/Chicago:20211118T113000
UID:submissions.supercomputing.org_SC21_sess161_pap420@linklings.com
SUMMARY:STM-Multifrontal QR: Streaming Task Mapping Multifrontal QR Factor
 ization Empowered by GCN
DESCRIPTION:Paper\n\nSTM-Multifrontal QR: Streaming Task Mapping Multifron
 tal QR Factorization Empowered by GCN\n\nLin, Yang, Wang, Tsai, Li\n\nMult
 ifrontal QR algorithm, which consists of symbolic analysis and numerical f
 actorization, is a high-performance algorithm for orthogonal factorizing s
 parse matrix. In this work, a graph convolutional network (GCN) for adapti
 vely selecting the optimal reordering algorithm is proposed in symbolic an
 alysis. Using our GCN adaptive classifier, the average numerical factoriza
 tion time is reduced by 20.78% compared with the default approach, and the
  additional memory overhead is approximately 4% higher than that of prior 
 work. Moreover, for numerical factorization, an optimized tasks stream par
 allel processing strategy is proposed, and a more efficient computing task
  mapping framework for NUMA architecture is adopted in this paper, which i
 s called STM-Multifrontal QR factorization. Numerical experiments on the T
 aiShan Server show average performance gains of 1.22x over the original Su
 iteSparseQR. Nearly 80% of datasets have achieved better performance compa
 red with the MKL sparse QR on Intel Xeon 6248.\n\nTag: Reproducibility Bad
 ge, Algorithms, Architectures, Numerical Algorithms\n\nRegistration Catego
 ry: Tech Program Reg Pass\n\nReproducibility Badges: Artifact Available, A
 rtifact Functional, Results Reproduced
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