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DTSTART;TZID=America/Chicago:20251118T163700
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UID:submissions.supercomputing.org_SC25_sess304_pap310@linklings.com
SUMMARY:A Nested Krylov Method Using Half-Precision Arithmetic
DESCRIPTION:Kengo Suzuki and Takeshi Iwashita (Kyoto University)\n\nLow-pr
 ecision computing is essential for efficiently utilizing memory bandwidth 
 and computing cores. While many mixed-precision algorithms have been devel
 oped for iterative sparse linear solvers, effectively leveraging half-prec
 ision (fp16) arithmetic remains challenging. This study introduces a novel
  nested Krylov approach that integrates the flexible GMRES and Richardson 
 methods in a deeply nested structure, progressively reducing precision fro
 m double-precision to fp16 toward the innermost solver. To avoid meaningle
 ss computations beyond precision limits, the low-precision inner solvers p
 erform only a few iterations per invocation, while the nested structure en
 sures their frequent execution. Numerical experiments show that incorporat
 ing fp16 into the approach directly enhances solver performance without co
 mpromising convergence, achieving speedups of up to 2.42 and 1.65 over dou
 ble-precision and double-single mixed-precision implementations, respectiv
 ely. Furthermore, the proposed method outperforms conventional mixed-preci
 sion Krylov solvers, CG, BiCGStab, and restarted FGMRES, by factors of up 
 to 2.47, 2.74, and 69.10, respectively.\n\nTag: Algorithms, Applications, 
 Architectures & Networks\n\nRecording: Livestreamed, Recorded\n\nRegistrat
 ion Category: Technical Program Reg Pass\n\nSession Chair: Hartwig Anzt (T
 echnical University of Munich; University of Tennessee, Knoxville)\n\n
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