Automatic performance tuning using the ATMathCoreLib tool: Two experimental studies related to dense symmetric eigensolvers
Masato Kobayashi, Yusuke Hirota, Shuhei Kudo, Takeo Hoshi, Yusaku Yamamoto · Concurrency and Computation Practice and Experience · 2023
Summary We consider automatic performance tuning of dense symmetric eigenvalue problems using ATMathCoreLib, which is a library to assist automatic tuning. We deal with two problems, namely, automatic code selection for the symmetric generalized eigenvalue problem in distributed‐memory parallel environments and automatic parameter tuning in tridiagonalization of dense symmetric matrices on multicore processors. As for the first problem, numerical experiments show that ATMathCoreLib can choose the fastest solver for a given computing environment and problem size quickly even if the fluctuation in the execution time is as high as 40%. As for the second problem, ATMathCoreLib was able to select nearly optimal combinations of the algorithm and its parameter reliably and efficiently for various computing environments and matrix sizes. The performance of auto‐tuning was further enhanced by incorporating a user‐provided execution‐time model into ATMathCoreLib.