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DTSTART;TZID=America/Chicago:20251118T155200
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UID:submissions.supercomputing.org_SC25_sess304_pap518@linklings.com
SUMMARY:Numerical Performance of the Implicitly Restarted Arnoldi Method i
 n OFP8, Bfloat16, Posit, and Takum Arithmetics
DESCRIPTION:Laslo Hunhold (University of Cologne), James Quinlan (Universi
 ty of Southern Maine), and Stefan Wesner (University of Cologne)\n\nThe co
 mputation of select eigenvalues and eigenvectors of large, sparse matrices
  is fundamental to a wide range of applications. Accordingly, evaluating t
 he numerical performance of emerging alternatives to the IEEE 754 floating
 -point standard, such as OFP8 (E4M3 and E5M2), bfloat16, and the tapered-p
 recision posit and takum formats, is of significant interest. Among the mo
 st widely used methods for this task is the implicitly restarted Arnoldi m
 ethod, as implemented in ARPACK.\n\nThis paper presents a comprehensive an
 d untailored evaluation based on two real-world datasets: the SuiteSparse 
 Matrix Collection, which includes matrices of varying sizes and condition 
 numbers, and the Network Repository, a large collection of graphs from pra
 ctical applications. The results demonstrate that the tapered-precision po
 sit and takum formats provide improved numerical performance, with takum a
 rithmetic avoiding several weaknesses observed in posits. While bfloat16 p
 erforms consistently better than float16, the OFP8 types are generally uns
 uitable for general-purpose computations.\n\nTag: Algorithms, Applications
 , Architectures & Networks\n\nRecording: Livestreamed, Recorded\n\nRegistr
 ation Category: Technical Program Reg Pass\n\nSession Chair: Hartwig Anzt 
 (Technical University of Munich; University of Tennessee, Knoxville)\n\n
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