Eigensolver performance comparison on Cray XC systems. (25th September 2018)

• Record Type: Journal Article
• Authors: Cook, Brandon; Kurth, Thorsten; Deslippe, Jack; Carrier, Pierre; Hill, Nick; Wichmann, Nathan
• Other Names: Thota Abhinav guestEditor.; He Yun guestEditor.; Hart David guestEditor.; Dahan Maytal guestEditor.; Navratil Paul guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 16(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-25
• Subjects: cray -- eigensolver -- ELPA -- performance -- scalapack; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4997
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Hermitian (symmetric) eigenvalue solvers are the core constituents of electronic structure, quantum‐chemistry, and other HPC applications such as Quantum ESPRESSO, VASP, CP2K, and NWChem to name a few. Our understanding of the performance of symmetric eigenvalue algorithms on various hardware is clearly important to the quantum chemistry or condensed matter physics community but in fact goes beyond that community. For instance, big data analytics is increasingly utilizing eigenvalues solvers, in the study of randomized singular value decomposition (SVD) or principal component analysis (PCA). Noise, vibration, and harshness (NVH) is another field where fast and efficient eigenvalue solvers are required. Most eigenvalue solver packages feature numerous different parameters which can be tuned for performance, eg, the number of nodes, number of total ranks, the decomposition of the matrix, etc. In this paper, we investigate the performance of different packages as well as the influence of these knobs on the solver performance.