The EVSL package for symmetric eigenvalue problems Yousef Saad Department of Computer Science and Engineering University of Minnesota

Transcription

1 The EVSL package for symmetric eigenvalue problems Yousef Saad Department of Computer Science and Engineering University of Minnesota 15th Copper Mountain Conference Mar. 28, 218

2 First: Joint work with Ruipeng Li, Yuanzhe Xi, and Luke Erlandson Application side: collaboration with Jia Shi, Maarten V. de Hoop (Rice) Support: NSF

3 Spectrum Slicing Context: very large number of eigenvalues to be computed Goal: compute spectrum by slices by applying filtering Apply Lanczos or Subspace iteration to problem: 1.8 φ(λ i ) Pol. of degree 32 approx δ(.5) in [ 1 1] φ(a)u = µu φ(t) a polynomial or rational function that enhances wanted eigenvalues φ ( λ ) λ i.2 λ 3 Copper Mountain,

4 Rationale. Eigenvectors on both ends of wanted spectrum need not be orthogonalized against each other : Idea: Get the spectrum by slices or windows [e.g., a few hundreds or thousands of pairs at a time] Note: Orthogonalization cost can be very high if we do not slice the spectrum 4 Copper Mountain,

5 Illustration: All eigenvalues in [, 1] of a 49 3 Laplacean 6 5 Computing all 1,971 e.v. s. in [, 1] Matvec Orth Total 4 Time Number of slices Note: This is a small pb. in a scalar environment. Effect likely much more pronounced in a fully parallel case. 5 Copper Mountain,

6 How do I slice my spectrum?.25 Slice spectrum into 8 with the DOS Answer: Use the spectral density, aka, Density Of States (DOS) DOS inexpensive to compute DOS We must have: ti+1 t i φ(t)dt = 1 n slices b a φ(t)dt 6 Copper Mountain,

7 Polynomial filtering: The δ-dirac function approach Obtain the LS approximation to the δ Dirac function Centered at some point (TBD) inside interval. 1.8 φ(λ i ) Pol. of degree 32 approx δ(.5) in [ 1 1] 1.8 Three filters using different smoothing No damping Jackson Lanczos σ φ ( λ ) λ i ρ k ( λ ) λ λ Damping: Jackson, Lanczos σ damping, or none. 7 Copper Mountain,

8 The soul of a new filter A few technical details Issue # one: balance the filter To facilitate the selection of wanted eigenvalues [Select λ s such that ρ(λ) > bar] we need to find γ so that ρ(ξ) ρ(η) = Procedure: Solve the equation ρ γ (ξ) ρ γ (η) = with respect to γ, accurately. Use Newton scheme ρ k ( λ ) ρ k ( λ ) λ ξ ξ η η λ 8 Copper Mountain,

9 Issue # two: Determine degree & polynomial (automatically) Start low then increase degree until value (s) at the boundary (ies) become small enough - Exple for [.833,.97..] 1.2 Degree = 3; Sigma damping 1.2 Degree = 4; Sigma damping 1.2 Degree = 7; Sigma damping Degree = 13; Sigma damping 1.2 Degree = 2; Sigma damping 1.2 Degree = 23; Sigma damping Copper Mountain,

10 Which Projection: Lanczos,w/o restarts, Subspace iteration,.. Options: Subspace iteration: quite appealing in some applications (e.g., electronic structure): Can re-use previous subspace. Simplest: (+ most efficient) Lanczos without restarts Lanczos with Thick-Restarting [TR Lanczos, Stathopoulos et al 98, Wu & Simon ] Crucial tool in TR Lanczos: deflation ( Locking ) Main idea: Keep extracting eigenvalues in interval [ξ, η] until none are left [remember: deflation] If filter is good: Can catch all eigenvalues in interval thanks to deflation + Lanczos. 1 Copper Mountain,

11 Polynomial filtered Lanczos: No-Restart version Degree = 23; Sigma damping Use Lanczos with full reorthogonalization on ρ(a). Eigenvalues of ρ(a): ρ(λ i ) Accept if ρ(λ i ) bar Ignore if ρ(λ i ) < bar ρ(λ) Unwanted eigenvalues Wanted 11 Copper Mountain,

12 Rational filters: Why? Consider a spectrum like this one: 1 9 Polynomial filtering utterly ineffective for this case Second issue: situation when Matrix-vector products are expensive Generalized eigenvalue problems. 12 Copper Mountain,

13 Alternative is to use rational filters: φ(z) = j α j z σ j φ(a) = j α j(a σ j I) 1 We now need to solve linear systems Tool: Cauchy integral representations of spectral projectors P = 1 2iπ Γ (A si) 1 ds Numer. integr. P P Use Krylov or S.I. on P Sakurai-Sugiura approach [Krylov] FEAST [Subs. iter.] (E. Polizzi) 13 Copper Mountain,

14 The Gauss viewpoint: Least-squares rational filters Given: poles σ 1, σ 2,, σ p Related basis functions φ j (z) = 1 z σ j Find φ(z) = p j=1 α jφ j (z) that minimizes w(t) h(t) φ(t) 2 dt h(t) = step function χ [ 1,1]. w(t)= weight function. For example a = 1, β =.1 w(t) = if t > a β if t 1 1 else Many advantages 14 Copper Mountain,

15 Spectrum Slicing and the EVSL project EVSL package now at version 1.1.x Uses polynomial and rational filtering: Each can be appealing in different situations. Spectrum slicing: Invokes Kernel Polynomial Method or Lanczos quadrature to cut the overall interval containing the spectrum into small sub-intervals φ ( λ ) Copper Mountain, λ

16 Levels of parallelism Macro task 1 Slice 1 Domain 1 Slice 2 Slice 3 Domain 2 Domain 3 Domain 4 The two main levels of parallelism in EVSL 16 Copper Mountain,

17 EVSL Main Contributors (version 1.1.+) & Support Ruipeng Li LLNL Yuanzhe Xi Post-doc (UMN) Luke Erlandson UG Intern (UMN) Work supported by NSF (also past work: DOE) See web-site for details: 17 Copper Mountain,

18 EVSL: current status & plans Version _1. Released in Sept. 216 Matrices in CSR format (only) Standard Hermitian problems (no generalized) Spectrum slicing with KPM (Kernel Polynomial Meth.) Trivial parallelism across slices with OpenMP Methods: Non-restart Lanczos polynomial & rational filters Thick-Restart Lanczos polynomial & rational filters Subspace iteration polynomial & rational filters 18 Copper Mountain,

19 Version _1.1.x V_1.1. Released back in August 217. general matvec [passed as function pointer] Ax = λbx Fortran (3) interface. Spectrum slicing by Lanczos and KPM Efficient Spectrum slicing for Ax = λbx (no solves with B). Version _1.2.x pevsl In progress Fully parallel version [MPI + openmp] 19 Copper Mountain,

20 Spectrum slicing and the EVSL package All eigenvalues in [, 1] of of a 49 3 discretized Laplacian eigs(a,1971, sa ): sec Solution: Use DOS to partition [, 1] into 5 slices Polynomial filtering from EVSL on Mesabi MSI, 23 threads/slice [a i, a i+1 ] CPU time (sec) # eigs matvec orth. total max residual [.,.37688] [.37688,.57428] [.57428,.73422] [.73422,.87389] [.87389, 1.] Grand tot. = 263 s. Time for slicing the spectrum: 1.22 sec. 2 Copper Mountain,

21 Computing the Earth normal modes Collaborative effort: Rice-UMN: J. Shi, R. Li, Y. Xi, YS, and M. V. De Hoop FEM model leads to a generalized eigenvalue problem: 21 Copper Mountain,

22 A s E T fs AT d E fs A d A p u s s u f = ω M 2 M f p e u s u f p e Want all eigen-values/vectors inside a given interval Issue 1: mass matrix has a large null space.. Issue 2: interior eigenvalue problem Solution for 1: change formulation of matrix problem [eliminate p e...] 22 Copper Mountain,

23 New formulation : {( ) ( ) As Efs ( ) } ( ) u A 1 E T s A p fs AT d } d u {{} f = Â ( ) ω 2 Ms M }{{ f } M ( ) u s u f Use polynomial filtering need to solve with M but severe scaling problems if direct solvers are used Hence: Replace action of M 1 by a low-deg. polynomial in M [to avoid direct solvers] 23 Copper Mountain,

24 Memory : parallel shift-invert and polynomial filtering Machine: Comet, SDSC Matrix size # Proc.s 591, , 157, , 425, , 778, , 37, Copper Mountain,

25 Recent: weak calability test for different solid (Mars-like) models on TACC Stampede2 nn/np Mat-size Av (ms) Eff. Mv (ms) Eff. M 1 v (µs) Eff. 2/96 1,38, /192 2,6, /384 3,894, /768 7,954, / ,89, /372 31,138, / ,381, / ,336, Copper Mountain,

26 Conclusion EVSL code available here: [Current version: version 1.1.1] EVSL Also on github (development) Plans: (1) Release fully parallel code; (2) Block versions; (3) Iterative solvers for rational filt.; (4) Nonhermitian case; Earth modes calculations done with fully parallel code Not quite ready for distribution A final note: Scalability issues with parallel direct solvers Needed: iterative solvers for the highly indefinite case 26 Copper Mountain,