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December 2022

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Einladung Informatik-Oberseminar: Beyond the rigid interfaces of
by Christos Psarras 22 Dec '22

22 Dec '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Monday, 23. January 2023, 15:30 Uhr Ort: Online (Zoom: https://umu.zoom.us/my/pauldj) Referent: Christos Psarras, M.Sc. International Research Training Group (IRTG-2379) Thema: Beyond the rigid interfaces of super-optimized building-block libraries. Our experiences in Chemometrics Abstract: The efficient computation of linear algebra expressions is a challenging task faced by many practitioners in scientific fields, such as engineering, image processing, and computational chemistry, to name a few. For most applications, mapping a target expression into a sequence of highly-optimized library routines (often referred to as "building-block" libraries, e.g., BLAS, LAPACK), is an approach that offers good computational performance as well as accuracy. However, in other applications, this approach inherently results in a vast under-utilization of the available computational resources, and thus reduced performance. In this talk, we emphasize on these, latter, applications, showcasing two occurrences that routinely arise in Chemometrics: the Canonical Polyadic Decomposition (CP) and Jackknife resampling of CP models. For the first occurrence, we describe the limitations of "mapping to building-blocks" when computing multiple, low-rank CP decompositions. After close collaboration with Chemometrics practitioners, we present a method (and algorithm), CP-CALS, which leverages information about their workflow, to overcome said limitations and achieve better performance. For the second occurrence, we describe the unique challenge of Jackknife resampling. We present a solution that addresses this challenge by making it possible to use CP-CALS to significantly increase performance, at the cost of slightly increasing the total amount of required computation. Through extensive experimentation with synthetic and real datasets on single-threaded and multi-threaded architectures, as well as on accelerators, we illustrate the improved efficiency and performance of our methods. Es laden ein: die Dozentinnen und Dozenten der Informatik

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