Composing Nonlinear Solvers

Transcription

1 Composing Nonlinear Solvers Matthew Knepley Computational and Applied Mathematics Rice University MIT Aeronautics and Astronautics Boston, MA May 10, 2016 Matt (Rice) PETSc MIT 1 / 69

2 What is PETSc? PETSc is one of the most popular software libraries in scientific computing. As a principal architect since 2001, I developed unstructured meshes (model, algorithms, implementation), nonlinear preconditioning (model, algorithms), FEM discretizations (data structures, solvers optimization), optimizations for multicore and GPU architectures. Matt (Rice) PETSc MIT 2 / 69

3 What is PETSc? Knepley, Karpeev, Sci. Prog., Brune, Knepley, Scott, SISC, Vertices Depth 0 Edges Depth 1 Faces Depth 2 Cells 0 1 Depth 3 As a principal architect since 2001, I developed unstructured meshes (model, algorithms, implementation), nonlinear preconditioning (model, algorithms), FEM discretizations (data structures, solvers optimization), optimizations for multicore and GPU architectures. Matt (Rice) PETSc MIT 2 / 69

4 What is PETSc? Brune, Knepley, Smith, and Tu, SIAM Review, Type Sym Statement Abbreviation Additive + x + α(m(f, x, b) x) M + N + β(n (F, x, b) x) Multiplicative M(F, N (F, x, b), b) M N Left Prec. L M( x N (F, x, b), x, b) M L N Right Prec. R M(F(N (F, x, b)), x, b) M R N Inner Lin. Inv. \ y = J( x) 1 r( x) = K( J( x), y 0, b) N \K As a principal architect since 2001, I developed unstructured meshes (model, algorithms, implementation), nonlinear preconditioning (model, algorithms), FEM discretizations (data structures, solvers optimization), optimizations for multicore and GPU architectures. Matt (Rice) PETSc MIT 2 / 69

5 What is PETSc? Aagaard, Knepley, and Williams, J. of Geophysical Research, As a principal architect since 2001, I developed unstructured meshes (model, algorithms, implementation), nonlinear preconditioning (model, algorithms), FEM discretizations (data structures, solvers optimization), optimizations for multicore and GPU architectures. Matt (Rice) PETSc MIT 2 / 69

6 What is PETSc? Knepley and Terrel, Transactions on Mathematical Software, As a principal architect since 2001, I developed unstructured meshes (model, algorithms, implementation), nonlinear preconditioning (model, algorithms), FEM discretizations (data structures, solvers optimization), optimizations for multicore and GPU architectures. Matt (Rice) PETSc MIT 2 / 69

7 Main Idea When you see an approximate solver, I see a preconditioner. Matt (Rice) PETSc MIT 3 / 69

8 Main Idea When you see an approximate solver, I see a preconditioner. Matt (Rice) PETSc MIT 3 / 69

9 Composable System for Scalable Preconditioners Stokes and KKT There are many approaches for saddle-point problems: Block preconditioners F B M u f Schur complement methods B T 0 0 p = 0 N 0 K T q Multigrid with special smoothers However, today it is hard to compare & combine them and combine in a hierarchical manner. For instance we might want, Matt (Rice) PETSc MIT 4 / 69

10 Composable System for Scalable Preconditioners Stokes and KKT There are many approaches for saddle-point problems: Block preconditioners F B M u f Schur complement methods B T 0 0 p = 0 N 0 K T q Multigrid with special smoothers However, today it is hard to compare & combine them and combine in a hierarchical manner. For instance we might want, Matt (Rice) PETSc MIT 4 / 69

11 Composable System for Scalable Preconditioners Stokes and KKT There are many approaches for saddle-point problems: Block preconditioners F B M u f Schur complement methods B T 0 0 p = 0 N 0 K T q Multigrid with special smoothers However, today it is hard to compare & combine them and combine in a hierarchical manner. For instance we might want, a Gauss-Siedel iteration between blocks of (u, p) and T, and a full Schur complement factorization for u and p. Matt (Rice) PETSc MIT 4 / 69

12 Composable System for Scalable Preconditioners Stokes and KKT There are many approaches for saddle-point problems: Block preconditioners F B M u f Schur complement methods B T 0 0 p = 0 N 0 K T q Multigrid with special smoothers However, today it is hard to compare & combine them and combine in a hierarchical manner. For instance we might want, an upper triangular Schur complement factorization for u and p, and geometric multigrid for the u block. Matt (Rice) PETSc MIT 4 / 69

13 Composable System for Scalable Preconditioners Stokes and KKT There are many approaches for saddle-point problems: Block preconditioners F B M u f Schur complement methods B T 0 0 p = 0 N 0 K T q Multigrid with special smoothers However, today it is hard to compare & combine them and combine in a hierarchical manner. For instance we might want, algebraic multigrid for the full (u, p) system, using a block triangular Gauss-Siedel smoother on each level, and use identity for the (p, p) block. Matt (Rice) PETSc MIT 4 / 69

14 Approach for efficient, robust, scalable linear solvers Need solvers to be: Composable: separately developed solvers may be easily combined, by non-experts, to form a more powerful solver Nested: outer solvers call inner solvers Hierarchical: outer solvers may iterate over all variables for a global problem, while nested inner solvers handle smaller subsets of physics, smaller physical subdomains, or coarser meshes Extensible: users can easily customize/extend Composable Linear Solvers for Multiphysics, Brown, Knepley, May, McInnes, Smith, IPDPS, Matt (Rice) PETSc MIT 5 / 69

15 FieldSplit Customization Analysis -pc_fieldsplit_<split num>_fields 2,1,5 -pc_fieldsplit_detect_saddle_point Synthesis -pc_fieldsplit_type -pc_fieldsplit_real_diagonal Use diagonal blocks of operator to build PC Schur complements -pc_fieldsplit_schur_precondition <self,user,diag> How to build preconditioner for S -pc_fieldsplit_schur_factorization_type <diag,lower,upper,full> Which off-diagonal parts of the block factorization to use Matt (Rice) PETSc MIT 6 / 69

16 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh ( A ) B B T 0 Matt (Rice) PETSc MIT 7 / 69

17 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Block-Jacobi (Exact), Cohouet & Chabard, IJNMF, ksp_type gmres -pc_type fieldsplit -pc_fieldsplit_type additive -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type lu -fieldsplit_pressure_ksp_type preonly -fieldsplit_pressure_pc_type jacobi ( ) A 0 0 I Matt (Rice) PETSc MIT 7 / 69

18 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Block-Jacobi (Inexact), Cohouet & Chabard, IJNMF, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type additive -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type preonly -fieldsplit_pressure_pc_type jacobi (Â ) 0 0 I Matt (Rice) PETSc MIT 7 / 69

19 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Gauss-Seidel (Inexact), Elman, DTIC, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type multiplicative -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type preonly -fieldsplit_pressure_pc_type jacobi (Â ) B 0 I Matt (Rice) PETSc MIT 7 / 69

20 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Gauss-Seidel (Inexact), Elman, DTIC, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type multiplicative -pc_fieldsplit_0_fields 1 -pc_fieldsplit_1_fields 0 -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type preonly -fieldsplit_pressure_pc_type jacobi ( ) I B T 0 Â Matt (Rice) PETSc MIT 7 / 69

21 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Diagonal Schur Complement, Olshanskii, et.al., Numer. Math., ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type diag -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type minres -fieldsplit_pressure_pc_type none ) (Â 0 0 Ŝ Matt (Rice) PETSc MIT 7 / 69

22 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Lower Schur Complement, May and Moresi, PEPI, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type lower -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type minres -fieldsplit_pressure_pc_type none ( ) Â 0 B T Ŝ Matt (Rice) PETSc MIT 7 / 69

23 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Upper Schur Complement, May and Moresi, PEPI, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type upper -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type gamg -fieldsplit_pressure_ksp_type minres -fieldsplit_pressure_pc_type none ) (Â B Ŝ Matt (Rice) PETSc MIT 7 / 69

24 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Uzawa Iteration, Uzawa, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type upper -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type lu -fieldsplit_pressure_ksp_type richardson -fieldsplit_pressure_ksp_max_its 1 ( A BŜ) Matt (Rice) PETSc MIT 7 / 69

25 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Full Schur Complement, Schur, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type full -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type lu -fieldsplit_pressure_ksp_rtol 1e-10 -fieldsplit_pressure_pc_type jacobi ( I 0 B T A 1 I ) ( A 0 0 S ) ( I A 1 ) B 0 I Matt (Rice) PETSc MIT 7 / 69

26 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh SIMPLE, Patankar and Spalding, IJHMT, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type full -fieldsplit_velocity_ksp_type preonly -fieldsplit_velocity_pc_type lu -fieldsplit_pressure_ksp_rtol 1e-10 -fieldsplit_pressure_pc_type jacobi -fieldsplit_pressure_inner_ksp_type preonly -fieldsplit_pressure_inner_pc_type jacobi -fieldsplit_pressure_upper_ksp_type preonly -fieldsplit_pressure_upper_pc_type jacobi ( ) ( ) ( I 0 A 0 I D 1 B T A 1 I 0 B T D 1 A B A B ) 0 I Matt (Rice) PETSc MIT 7 / 69

27 Solver Configuration: No New Code ex62: P 2 /P 1 Stokes Problem on Unstructured Mesh Least-Squares Commutator, Kay, Loghin and Wathen, SISC, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_schur_factorization_type full -pc_fieldsplit_schur_precondition self -fieldsplit_velocity_ksp_type gmres -fieldsplit_velocity_pc_type lu -fieldsplit_pressure_ksp_rtol 1e-5 -fieldsplit_pressure_pc_type lsc ( ) ( ) ( I 0 A 0 I A 1 ) B B T A 1 I 0 ŜLSC 0 I Matt (Rice) PETSc MIT 7 / 69

28 Solver Configuration: No New Code ex31: P 2 /P 1 Stokes Problem with Temperature on Unstructured Mesh Additive Schwarz + Full Schur Complement, Elman, Howle, Shadid, Shuttleworth, and Tuminaro, SISC, ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type additive -pc_fieldsplit_0_fields 0,1 -pc_fieldsplit_1_fields 2 -fieldsplit_0_ksp_type fgmres -fieldsplit_0_pc_type fieldsplit -fieldsplit_0_pc_fieldsplit_type schur -fieldsplit_0_pc_fieldsplit_schur_factorization_type full -fieldsplit_0_fieldsplit_velocity_ksp_type preonly -fieldsplit_0_fieldsplit_velocity_pc_type lu -fieldsplit_0_fieldsplit_pressure_ksp_rtol 1e-10 -fieldsplit_0_fieldsplit_pressure_pc_type jacobi -fieldsplit_temperature_ksp_type preonly -fieldsplit_temperature_pc_type lu ( ) ( ) (I I 0 Â 0 A 1 ) B B T A 1 I 0 Ŝ 0 0 I 0 L T Matt (Rice) PETSc MIT 7 / 69

29 Solver Configuration: No New Code ex31: P 2 /P 1 Stokes Problem with Temperature on Unstructured Mesh Upper Schur Comp. + Full Schur Comp. + Least-Squares Comm. -ksp_type fgmres -pc_type fieldsplit -pc_fieldsplit_type schur -pc_fieldsplit_0_fields 0,1 -pc_fieldsplit_1_fields 2 -pc_fieldsplit_schur_factorization_type upper -fieldsplit_0_ksp_type fgmres -fieldsplit_0_pc_type fieldsplit -fieldsplit_0_pc_fieldsplit_type schur -fieldsplit_0_pc_fieldsplit_schur_factorization_type full -fieldsplit_0_fieldsplit_velocity_ksp_type preonly -fieldsplit_0_fieldsplit_velocity_pc_type lu -fieldsplit_0_fieldsplit_pressure_ksp_rtol 1e-10 -fieldsplit_0_fieldsplit_pressure_pc_type jacobi -fieldsplit_temperature_ksp_type gmres -fieldsplit_temperature_pc_type lsc ( I 0 B T A 1 I ) ( Â 0 0 Ŝ ) (I A 1 B 0 I ) G 0 Ŝ LSC Matt (Rice) PETSc MIT 7 / 69

30 The Great Solver Schism: Monolithic or Split? Monolithic Direct solvers Coupled Schwarz Coupled Neumann-Neumann (use unassembled matrices) Coupled Multigrid Split Physics-split Schwarz (based on relaxation) Physics-split Schur (based on factorization) SIMPLE, PCD, LSC segregated smoothers Augmented Lagrangian Need to understand Local spectral properties Compatibility properties Global coupling strengths Matt (Rice) PETSc MIT 8 / 69

31 Outline Composition Strategies 1 Composition Strategies 2 Theory 3 Experiments 4 Concluding THoughts Matt (Rice) PETSc MIT 9 / 69

32 Composition Strategies Abstract System Out prototypical nonlinear equation is: F( x) = b (1) and we define the residual as r( x) = F( x) b (2) Matt (Rice) PETSc MIT 10 / 69

33 Composition Strategies Abstract System Out prototypical nonlinear equation is: F( x) = b (1) and we define the (linear) residual as r( x) = A x b (3) Matt (Rice) PETSc MIT 10 / 69

34 Composition Strategies Linear Left Preconditioning The modified equation becomes P (A x 1 ) b = 0 (4) Matt (Rice) PETSc MIT 11 / 69

35 Composition Strategies Linear Left Preconditioning The modified defect correction equation becomes P (A x 1 i ) b = x i+1 x i (5) Matt (Rice) PETSc MIT 11 / 69

36 Composition Strategies Additive Combination The linear iteration x i+1 = x i (αp 1 + βq 1 )(A x i b) (6) becomes the nonlinear iteration Matt (Rice) PETSc MIT 12 / 69

37 Composition Strategies Additive Combination The linear iteration x i+1 = x i (αp 1 + βq 1 ) r i (7) becomes the nonlinear iteration Matt (Rice) PETSc MIT 12 / 69

38 Composition Strategies Additive Combination The linear iteration x i+1 = x i (αp 1 + βq 1 ) r i (7) becomes the nonlinear iteration x i+1 = x i +α(n (F, x i, b) x i )+β(m(f, x i, b) x i ) (8) Matt (Rice) PETSc MIT 12 / 69

39 Composition Strategies Nonlinear Left Preconditioning From the additive combination, we have P 1 r = x i N (F, x i, b) (9) so we define the preconditioning operation as r L x N (F, x, b) (10) Matt (Rice) PETSc MIT 13 / 69

40 Composition Strategies Multiplicative Combination The linear iteration x i+1 = x i (P 1 + Q 1 Q 1 AP 1 ) r i (11) becomes the nonlinear iteration Matt (Rice) PETSc MIT 14 / 69

41 Composition Strategies Multiplicative Combination The linear iteration x i+1/2 = x i P 1 r i (12) becomes the nonlinear iteration x i = x i+1/2 Q 1 r i+1/2 (13) Matt (Rice) PETSc MIT 14 / 69

42 Composition Strategies Multiplicative Combination The linear iteration x i+1/2 = x i P 1 r i (12) x i = x i+1/2 Q 1 r i+1/2 (13) becomes the nonlinear iteration x i+1 = M(F, N (F, x i, b), b) (14) Matt (Rice) PETSc MIT 14 / 69

43 Composition Strategies Nonlinear Right Preconditioning For the linear case, we have AP 1 y = b (15) x = P 1 y (16) so we define the preconditioning operation as y = M(F(N (F,, b)), x i, b) (17) x = N (F, y, b) (18) Matt (Rice) PETSc MIT 15 / 69

44 Composition Strategies Nonlinear Preconditioning Type Sym Statement Abbreviation Additive + x + α(m(f, x, b) x) M + N + β(n (F, x, b) x) Multiplicative M(F, N (F, x, b), b) M N Left Prec. L M( x N (F, x, b), x, b) M L N Right Prec. R M(F(N (F, x, b)), x, b) M R N Inner Lin. Inv. \ y = J( x) 1 r( x) = K( J( x), y 0, b) N \K Composing Scalable Nonlinear Algebraic Solvers, Brune, Knepley, Smith, and Tu, SIAM Review, Matt (Rice) PETSc MIT 16 / 69

45 Outline Theory 1 Composition Strategies 2 Theory Convergence Rates Induction Theorem 3 Experiments 4 Concluding THoughts Matt (Rice) PETSc MIT 17 / 69

46 Outline Theory Convergence Rates 2 Theory Convergence Rates Induction Theorem Matt (Rice) PETSc MIT 18 / 69

47 Rate of Convergence Theory Convergence Rates What should be a Rate of Convergence? [Ptak, 1977]: 1 It should relate quantities which may be measured or estimated during the actual process 2 It should describe accurately in particular the initial stage of the process, not only its asymptotic behavior... x n+1 x c x n x q Matt (Rice) PETSc MIT 19 / 69

48 Rate of Convergence Theory Convergence Rates What should be a Rate of Convergence? [Ptak, 1977]: 1 It should relate quantities which may be measured or estimated during the actual process 2 It should describe accurately in particular the initial stage of the process, not only its asymptotic behavior... x n+1 x n c x n x n 1 q Matt (Rice) PETSc MIT 19 / 69

49 Rate of Convergence Theory Convergence Rates What should be a Rate of Convergence? [Ptak, 1977]: 1 It should relate quantities which may be measured or estimated during the actual process 2 It should describe accurately in particular the initial stage of the process, not only its asymptotic behavior... x n+1 x n ω( x n x n 1 ) where we have for all r (0, R] σ(r) = ω (n) (r) < n=0 Matt (Rice) PETSc MIT 19 / 69

50 Nondiscrete Induction Theory Convergence Rates Define an approximate set Z (r), where x Z (0) implies f (x ) = 0. Matt (Rice) PETSc MIT 20 / 69

51 Nondiscrete Induction Theory Convergence Rates Define an approximate set Z (r), where x Z (0) implies f (x ) = 0. For Newton s method, we use { } Z (r) = x f (x) 1 f (x) r, d(f (x)) h(r), x x 0 g(r), where d(a) = inf Ax, x 1 and h(r) and g(r) are positive functions. Matt (Rice) PETSc MIT 20 / 69

52 Nondiscrete Induction Theory Convergence Rates Define an approximate set Z (r), where x Z (0) implies f (x ) = 0. For r (0, R], Z (r) U(Z (ω(r)), r) implies Z (r) U(Z (0), σ(r)). Matt (Rice) PETSc MIT 20 / 69

53 Nondiscrete Induction Theory Convergence Rates For the fixed point iteration x n+1 = Gx n, if I have x 0 Z (r 0 ) and for x Z (r), Gx x r Gx Z (ω(r)) then Matt (Rice) PETSc MIT 21 / 69

54 Nondiscrete Induction Theory Convergence Rates For the fixed point iteration x n+1 = Gx n, if I have x 0 Z (r 0 ) and for x Z (r), Gx x r Gx Z (ω(r)) then x Z (0) x n Z (ω (n) (r 0 )) Matt (Rice) PETSc MIT 21 / 69

55 Nondiscrete Induction Theory Convergence Rates For the fixed point iteration x n+1 = Gx n, if I have x 0 Z (r 0 ) and for x Z (r), Gx x r Gx Z (ω(r)) then x n+1 x n ω (n) (r 0 ) x n x σ(ω (n) (r 0 )) Matt (Rice) PETSc MIT 21 / 69

56 Nondiscrete Induction Theory Convergence Rates For the fixed point iteration x n+1 = Gx n, if I have x 0 Z (r 0 ) and for x Z (r), Gx x r Gx Z (ω(r)) then x n x σ(ω( x n x n 1 )) = σ( x n x n 1 ) x n x n 1 Matt (Rice) PETSc MIT 21 / 69

57 Newton s Method Theory Convergence Rates ω N (r) = cr 2 Matt (Rice) PETSc MIT 22 / 69

58 Newton s Method Theory Convergence Rates where r 2 ω N (r) = 2 r 2 + a 2 σ N (r) = r + r 2 + a 2 a a = 1 k 0 1 2k0 r 0, k 0 is the (scaled) Lipschitz constant for f, and r 0 is the (scaled) initial residual. Matt (Rice) PETSc MIT 22 / 69

59 Newton s Method Theory Convergence Rates r 2 ω N (r) = 2 r 2 + a 2 σ N (r) = r + r 2 + a 2 a This estimate is tight in that the bounds hold with equality for some function f, using initial guess f (x) = x 2 a 2 x 0 = 1 k 0. Also, if equality is attained for some n 0, this holds for all n n 0. Matt (Rice) PETSc MIT 22 / 69

60 Newton s Method Theory Convergence Rates r 2 ω N (r) = 2 r 2 + a 2 σ N (r) = r + r 2 + a 2 a If r a, meaning we have an inaccurate guess, ω N (r) 1 2 r, whereas if r a, meaning we are close to the solution, ω N (r) 1 2a r 2. Matt (Rice) PETSc MIT 22 / 69

61 Left vs. Right Theory Convergence Rates Left: F(x) = x N (F, x, b) Right: x = y = N (F, x, b) Heisenberg vs. Schrödinger Picture Matt (Rice) PETSc MIT 23 / 69

62 Left vs. Right Theory Convergence Rates Left: F(x) = x N (F, x, b) Right: x = y = N (F, x, b) Heisenberg vs. Schrödinger Picture Matt (Rice) PETSc MIT 23 / 69

63 M R N Theory Convergence Rates We start with x Z (r), apply N so that y Z (ω N (r)), and then apply M so that x Z (ω M (ω N (r))). Thus we have ω M R N = ω M ω N Matt (Rice) PETSc MIT 24 / 69

64 Outline Theory Induction Theorem 2 Theory Convergence Rates Induction Theorem Matt (Rice) PETSc MIT 25 / 69

65 Non-Abelian Theory Induction Theorem N R NRICH r 2 ω N ω NRICH = 1 2 r 2 + a cr, 2 = 1 c 2 r 2 2 c2 r 2 + a 2, = 1 cr 2 2 r 2 + (a/c) 2, = 1 2 c r 2 r 2 + ã 2, Matt (Rice) PETSc MIT 26 / 69

66 Non-Abelian Theory Induction Theorem N R NRICH: 1 2 c r 2 r 2 +ã 2 NRICH R N r 2 ω NRICH ω N = cr 1 2 r 2 + a 2, = 1 2 c r 2 r 2 + a 2, = 1 2 c r 2 r 2 + a 2. Matt (Rice) PETSc MIT 27 / 69

67 Non-Abelian Theory Induction Theorem N R NRICH: 1 2 c r 2 r 2 +ã 2 NRICH R N : 1 2 c r 2 r 2 +a 2 The first method also changes the onset of second order convergence. Matt (Rice) PETSc MIT 28 / 69

68 Theory Induction Theorem Composed Rates of Convergence Theorem If ω 1 and ω 2 are convex rates of convergence, then ω = ω 1 ω 2 is a rate of convergence. Matt (Rice) PETSc MIT 29 / 69

69 Theory Induction Theorem Composed Rates of Convergence Theorem If ω 1 and ω 2 are convex rates of convergence, then ω = ω 1 ω 2 is a rate of convergence. First we show that ω(s) s r ω(r), which means that convex rates of convergence are non-decreasing. This implies that compositions of convex rates of convergence are also convex and non-decreasing. Matt (Rice) PETSc MIT 29 / 69

70 Theory Induction Theorem Composed Rates of Convergence Theorem If ω 1 and ω 2 are convex rates of convergence, then ω = ω 1 ω 2 is a rate of convergence. Then we show that by contradiction. ω(r) < r r (0, R) Matt (Rice) PETSc MIT 29 / 69

71 Theory Induction Theorem Composed Rates of Convergence Theorem If ω 1 and ω 2 are convex rates of convergence, then ω = ω 1 ω 2 is a rate of convergence. This is enough to show that and in fact ω 1 (ω 2 (r)) < ω 1 (r), (ω 1 ω 2 ) (n) (r) < ω (n) 1 (r). Matt (Rice) PETSc MIT 29 / 69

72 Theory Induction Theorem Multidimensional Induction Theorem Preconditions Theorem Let p (1 for our case) and m (2 for our case) be two positive integers, X be a complete metric space and D X p, G : D X p and F : D X p+1 be defined by Fu = (u, Gu), F k = P k F, p + 1 k m, the components of F, P = P m, Z (r) D for each r T p, ω be a rate of convergence of type (p, m) on T, u 0 D and r 0 T p. Matt (Rice) PETSc MIT 30 / 69

73 Theory Induction Theorem Multidimensional Induction Theorem Theorem If the following conditions hold u 0 Z (r 0 ), PFZ (r) Z ( ω(r)), F k u F k+1 u ω k (r), for all r T p, u Z (r), and k = 0,..., m 1, then 1 u 0 is admissible, and x X such that (P k u n ) n 0 x, 2 and the following relations hold for n > 1, Pu n Z ( ω(r 0 )), P k u n P k+1 u n ω (n) k (r 0 ), 0 k m 1, P k u n x σ k ( ω(r 0 )), 0 k m; Matt (Rice) PETSc MIT 31 / 69

74 Theory Induction Theorem Multidimensional Induction Theorem Theorem If the following conditions hold u 0 Z (r 0 ), PFZ (r) Z ( ω(r)), F k u F k+1 u ω k (r), for all r T p, u Z (r), and k = 0,..., m 1, then 1 u 0 is admissible, and x X such that (P k u n ) n 0 x, 2 and the following relations hold for n > 1, P k u n x σ k (r n ), 0 k m. where r n T p and Pu n 1 Z (r n ). Matt (Rice) PETSc MIT 31 / 69

75 Theory Induction Theorem Multidimensional Induction Theorem Theorem If the following conditions hold u 0 Z (r 0 ), PFZ (r) Z ( ω(r)), F k u F k+1 u ω k (r), for all r T p, u Z (r), and k = 0,..., m 1, then 1 u 0 is admissible, and x X such that (P k u n ) n 0 x, 2 and the following relations hold for n > 1, Pu n Z ( ω(r 0 )), P k u n P k+1 u n ω (n) k (r 0 ), 0 k m 1, P k u n x σ k ( ω(r 0 )), 0 k m; Matt (Rice) PETSc MIT 31 / 69

76 Theory Induction Theorem Multidimensional Induction Theorem Theorem If the following conditions hold u 0 Z (r 0 ), PFZ (r) Z (ω ψ(r)), F 0 u F 1 u r, F 1 u F 2 u ψ(r), for all r T p, u Z (r), and k = 0,..., m 1, then 1 u 0 is admissible, and x X such that (P k u n ) n 0 x, 2 and the following relations hold for n > 1, Pu n Z ( ω(r 0 )), P k u n P k+1 u n ω (n) k (r 0 ), 0 k m 1, P k u n x σ k ( ω(r 0 )), 0 k m; Matt (Rice) PETSc MIT 31 / 69

77 Theory Composed Newton Methods Induction Theorem Theorem Suppose that we have two nonlinear solvers M, Z 1, ω, N, Z 0, ψ, and consider M R N, meaning a single step of N for each step of M. Concretely, take M to be the Newton iteration, and N the Chord method. Then the assumptions of the theorem above are satisfied using Z = Z 1 and ω(r) = {ψ(r), ω ψ(r)}, giving us the existence of a solution, and both a priori and a posteriori bounds on the error. Matt (Rice) PETSc MIT 32 / 69

78 Example Theory Induction Theorem f (x) = x 2 + ( ) 2 n x n+1 x n x n+1 x n w (n) (r 0 ) x n x s(w (n) (r 0 )) e+00 < < e-01 < < e-01 < < e-01 < < e-01 < < e-02 < < e-02 < < e-04 < < e-06 < < e-11 < < e+00 < < Matt (Rice) PETSc MIT 33 / 69

79 Example Theory Induction Theorem Matrix iterations also 1D scalar once you diagonalize Pták s nondiscrete induction and its application to matrix iterations, Liesen, IMA J. Num. Anal., Matt (Rice) PETSc MIT 34 / 69

80 Outline Experiments 1 Composition Strategies 2 Theory 3 Experiments Composition Multilevel Magma Dynamics 4 Concluding THoughts Matt (Rice) PETSc MIT 35 / 69

81 Outline Experiments Composition 3 Experiments Composition Multilevel Magma Dynamics Matt (Rice) PETSc MIT 36 / 69

82 SNES ex16 3D Large Deformation Elasticity Experiments Composition Ω F S : v dω + loading e y v dω = 0 (19) Ω F Deformation gradient S Second Piola-Kirchhoff tensor Saint Venant-Kirchhoff model of hyperelasticity Ω -arc angle subsection of a cylindrical shell -height thickness -rad inner radius -width width Matt (Rice) PETSc MIT 37 / 69

83 Experiments Large Deformation Elasticity Composition Unstressed and stressed configurations for the elasticity test problem. Matt (Rice) PETSc MIT 38 / 69

84 Experiments Large Deformation Elasticity Composition Coloration indicates vertical displacement in meters. Matt (Rice) PETSc MIT 38 / 69

85 Experiments Large Deformation Elasticity Composition P. Wriggers, Nonlinear Finite Element Methods, Springer, Matt (Rice) PETSc MIT 38 / 69

86 Experiments Large Deformation Elasticity Running Composition SNES example 16: cd src/snes/examples/tutorials make ex16./ex16 -da_grid_x 401 -da_grid_y 9 -da_grid_z 9 -height 3 -width 3 -rad 100 -young 100 -poisson 0.2 -loading -1 -ploading 0 Matt (Rice) PETSc MIT 39 / 69

87 Experiments Plain SNES Convergence Composition (N \K MG) and NCG (NEWT\K MG) NCG r(x) Time (sec) Matt (Rice) PETSc MIT 40 / 69

88 Experiments Plain SNES Convergence Composition Solver T N. It L. It Func Jac PC NPC NCG (N \K MG) Matt (Rice) PETSc MIT 40 / 69

89 Experiments Composition Composed SNES Convergence NCG(10) + (N \K MG) and NCG(10) (N \K MG) NCG(10)+(NEWT\K MG) NCG(10)*(NEWT\K MG) r(x) Time (sec) Matt (Rice) PETSc MIT 41 / 69

90 Experiments Composition Composed SNES Convergence Solver T N. It L. It Func Jac PC NPC NCG (N \K MG) NCG(10) (N \K MG) NCG(10) (N \K MG) Matt (Rice) PETSc MIT 41 / 69

91 Experiments Composition Peconditioned SNES Convergence NGMRES R (N \K MG) and NCG L (N \K MG) NGMRES R(NEWT\K MG) NCG L(NEWT\K MG) r(x) Time (sec) Matt (Rice) PETSc MIT 42 / 69

92 Experiments Composition Peconditioned SNES Convergence Solver T N. It L. It Func Jac PC NPC NCG (N \K MG) NCG(10) (N \K MG) NCG(10) (N \K MG) NGMRES R (N \K MG) NCG L (N \K MG) Matt (Rice) PETSc MIT 42 / 69

93 Outline Experiments Multilevel 3 Experiments Composition Multilevel Magma Dynamics Matt (Rice) PETSc MIT 43 / 69

94 SNES ex19 Driven Cavity Flow Experiments Multilevel u + Ω = 0 Ω + ( uω) GR x T = 0 T + PR ( ut ) = 0 Matt (Rice) PETSc MIT 44 / 69

95 SNES ex19 Driven Cavity Flow Experiments Multilevel u + Ω = 0 Ω + ( uω) GR x T = 0 T + PR ( ut ) = 0 Matt (Rice) PETSc MIT 44 / 69

96 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e2 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view Matt (Rice) PETSc MIT 45 / 69

97 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e2 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm e-08 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 7 Matt (Rice) PETSc MIT 45 / 69

98 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e4 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view Matt (Rice) PETSc MIT 45 / 69

99 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e4 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm 4.417e-08 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 7 Matt (Rice) PETSc MIT 45 / 69

100 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e5 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view Matt (Rice) PETSc MIT 45 / 69

101 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e5 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -snes_monitor_short -snes_converged_reason -snes_view lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve did not converge due to DIVERGED_LINEAR_SOLVE iterations 0 Matt (Rice) PETSc MIT 45 / 69

102 Driven Cavity Problem Experiments Multilevel SNES ex19.c./ex19 -lidvelocity 100 -grashof 1e5 -da_grid_x 16 -da_grid_y 16 -da_refine 2 -pc_type lu -snes_monitor_short -snes_converged_reason -snes_view lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm Matt (Rice) PETSc MIT 45 / 69

103 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type newtonls -snes_converged_reason -pc_type lu lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm Matt (Rice) PETSc MIT 46 / 69

104 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type fas -snes_converged_reason -fas_levels_snes_type gs -fas_levels_snes_max_it 6 lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm Nonlinear solve did not converge due to DIVERGED_INNER iterations 4 Matt (Rice) PETSc MIT 46 / 69

105 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type fas -snes_converged_reason -fas_levels_snes_type gs -fas_levels_snes_max_it 6 -fas_coarse_snes_converged_reason lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 12 1 SNES Function norm Nonlinear solve did not converge due to DIVERGED_MAX_IT its 50 2 SNES Function norm Nonlinear solve did not converge due to DIVERGED_MAX_IT its 50 3 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 22 4 SNES Function norm Nonlinear solve did not converge due to DIVERGED_LINE_SEARCH its 42 Nonlinear solve did not converge due to DIVERGED_INNER iterations 4 Matt (Rice) PETSc MIT 46 / 69

106 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type fas -snes_converged_reason -fas_levels_snes_type gs -fas_levels_snes_max_it 6 -fas_coarse_snes_linesearch_type basic -fas_coarse_snes_converged_reason lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 5 48 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 6 49 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 5 50 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 6. Matt (Rice) PETSc MIT 46 / 69

107 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type nrichardson -npc_snes_max_it 1 -snes_converged_reason -npc_snes_type fas -npc_fas_coarse_snes_converged_reason -npc_fas_levels_snes_type gs -npc_fas_levels_snes_max_it 6 -npc_fas_coarse_snes_linesearch_type basic lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 6 1 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 27 2 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its SNES Function norm e-05 Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE its 2 44 SNES Function norm e-05 Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE its 2 45 SNES Function norm e-06 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 45 Matt (Rice) PETSc MIT 46 / 69

108 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type ngmres -npc_snes_max_it 1 -snes_converged_reason -npc_snes_type fas -npc_fas_coarse_snes_converged_reason -npc_fas_levels_snes_type gs -npc_fas_levels_snes_max_it 6 -npc_fas_coarse_snes_linesearch_type basic lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 6 1 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 13 2 SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its SNES Function norm Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 2 28 SNES Function norm e-05 Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE its 2 29 SNES Function norm e-05 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 29 Matt (Rice) PETSc MIT 46 / 69

109 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type ngmres -npc_snes_max_it 1 -snes_converged_reason -npc_snes_type fas -npc_fas_coarse_snes_converged_reason -npc_fas_levels_snes_type newtonls -npc_fas_levels_snes_max_it 6 -npc_fas_levels_snes_linesearch_type basic -npc_fas_levels_snes_max_linear_solve_fail 30 -npc_fas_levels_ksp_max_it 20 -npc_fas_levels_snes_converged_reason -npc_fas_coarse_snes_linesearch_type basic lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm Nonlinear solve did not converge due to DIVERGED_MAX_IT its 6. Nonlinear solve converged due to CONVERGED_SNORM_RELATIVE its 1. 1 SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm e-06 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 Matt (Rice) PETSc MIT 46 / 69

110 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type composite -snes_composite_type additiveoptimal -snes_composite_sneses fas,newtonls -snes_converged_reason -sub_0_fas_levels_snes_type gs -sub_0_fas_levels_snes_max_it 6 -sub_0_fas_coarse_snes_linesearch_type basic -sub_1_snes_linesearch_type basic -sub_1_pc_type mg lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm e-08 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 7 Matt (Rice) PETSc MIT 46 / 69

111 Experiments Nonlinear Preconditioning Multilevel./ex19 -lidvelocity 100 -grashof 5e4 -da_refine 4 -snes_monitor_short -snes_type composite -snes_composite_type multiplicative -snes_composite_sneses fas,newtonls -snes_converged_reason -sub_0_fas_levels_snes_type gs -sub_0_fas_levels_snes_max_it 6 -sub_0_fas_coarse_snes_linesearch_type basic -sub_1_snes_linesearch_type basic -sub_1_pc_type mg lid velocity = 100, prandtl # = 1, grashof # = SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm SNES Function norm e-08 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE iterations 5 Matt (Rice) PETSc MIT 46 / 69

112 Experiments Nonlinear Preconditioning Multilevel Solver T N. It L. It Func Jac PC NPC (N \K MG) NGMRES R (N \K MG) FAS FAS + (N \K MG) FAS (N \K MG) NRICH L FAS NGMRES R FAS Matt (Rice) PETSc MIT 46 / 69

113 Experiments Nonlinear Preconditioning Multilevel See discussion in: Composing Scalable Nonlinear Algebraic Solvers, Peter Brune, Matthew Knepley, Barry Smith, and Xuemin Tu, SIAM Review, 57(4), , Matt (Rice) PETSc MIT 46 / 69

114 Outline Experiments Magma Dynamics 3 Experiments Composition Multilevel Magma Dynamics Matt (Rice) PETSc MIT 47 / 69

115 Magma Dynamics Experiments Magma Dynamics Couples scales Subduction Magma Migration Physics Incompressible fluid Porous solid Variable porosity Deforming matrix Compaction pressure Code generation FEniCS Multiphysics Preconditioning PETSc FieldSplit y (a) x z a Katz (b) 300 km kg yr km 100 km Matt (Rice) PETSc MIT 48 / 69

116 Magma Dynamics Experiments Magma Dynamics Couples scales Subduction Magma Migration Physics Incompressible fluid Porous solid Variable porosity Deforming matrix Compaction pressure Code generation FEniCS Multiphysics Preconditioning PETSc FieldSplit a Katz, Speigelman a Matt (Rice) PETSc MIT 48 / 69

117 Experiments Dimensional Formulation Magma Dynamics ) p ζ φ ( v S ) (2η φ ɛ S = 0 ( K ) φ µ p + v S = 0 φ t (1 φ) v S = 0 Matt (Rice) PETSc MIT 49 / 69

118 Closure Conditions Experiments Magma Dynamics K φ = K 0 ( φ φ 0 ) n η φ = η 0 exp ( λ(φ φ 0 )) ζ φ = ζ 0 ( φ φ 0 ) m Matt (Rice) PETSc MIT 50 / 69

119 Experiments Nondimensional Formulation Magma Dynamics ( ( ) ) m φ ) p v S (2e λ(φ φ 0) ɛ S = 0 φ 0 ( ) n ) ( R2 φ p + v S = 0 r ζ + 4/3 φ 0 φ t (1 φ) v S = 0 Matt (Rice) PETSc MIT 51 / 69

120 Experiments Initial and Boundary conditions Magma Dynamics Initially φ = φ 0 + A cos( k x) where A φ 0 and on the top and bottom boundary K φ p ˆn = 0 v S = ± γ 2 ˆx Matt (Rice) PETSc MIT 52 / 69

121 Newton options Experiments Magma Dynamics -snes_monitor -snes_converged_reason -snes_type newtonls -snes_linesearch_type bt -snes_fd_color -snes_fd_color_use_mat -mat_coloring_type greedy -ksp_rtol 1.0e-10 -ksp_monitor -ksp_gmres_restart 200 -pc_type fieldsplit -pc_fieldsplit_0_fields 0,2 -pc_fieldsplit_1_fields 1 -pc_fieldsplit_type schur -pc_fieldsplit_schur_precondition selfp -pc_fieldsplit_schur_factorization_type full -fieldsplit_0_pc_type lu -fieldsplit_pressure_ksp_rtol 1.0e-9 -fieldsplit_pressure_pc_type gamg -fieldsplit_pressure_ksp_monitor -fieldsplit_pressure_ksp_gmres_restart 100 -fieldsplit_pressure_ksp_max_it 200 Matt (Rice) PETSc MIT 53 / 69

122 Newton options Separate porosity Experiments Magma Dynamics -pc_type fieldsplit -pc_fieldsplit_0_fields 0,1 -pc_fieldsplit_1_fields 2 -pc_fieldsplit_type multiplicative -fieldsplit_0_pc_type fieldsplit -fieldsplit_0_pc_fieldsplit_type schur -fieldsplit_0_pc_fieldsplit_schur_precondition selfp -fieldsplit_0_pc_fieldsplit_schur_factorization_type full -fieldsplit_0_fieldsplit_velocity_pc_type lu -fieldsplit_0_fieldsplit_pressure_ksp_rtol 1.0e-9 -fieldsplit_0_fieldsplit_pressure_pc_type gamg -fieldsplit_0_fieldsplit_pressure_ksp_monitor -fieldsplit_0_fieldsplit_pressure_ksp_gmres_restart 100 -fieldsplit_fieldsplit_0_pressure_ksp_max_it 200 Matt (Rice) PETSc MIT 54 / 69

123 Experiments Early Newton convergence Magma Dynamics 0 TS dt 0.01 time 0 0 SNES Function norm e-03 Linear pressure_ solve converged due to CONVERGED_RTOL its 10 0 KSP Residual norm e+00 Linear pressure_ solve converged due to CONVERGED_RTOL its 10 1 KSP Residual norm e-03 Linear pressure_ solve converged due to CONVERGED_RTOL its 11 2 KSP Residual norm e-13 Linear solve converged due to CONVERGED_RTOL its 2 1 SNES Function norm e-06 Linear pressure_ solve converged due to CONVERGED_RTOL its 8 0 KSP Residual norm e-02 Linear pressure_ solve converged due to CONVERGED_RTOL its 8 1 KSP Residual norm e-07 Linear pressure_ solve converged due to CONVERGED_RTOL its 8 2 KSP Residual norm e-14 Linear solve converged due to CONVERGED_RTOL its 2 2 SNES Function norm e-11 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 2 1 TS dt 0.01 time 0.01 Matt (Rice) PETSc MIT 55 / 69

124 Experiments Later Newton convergence Magma Dynamics 0 TS dt 0.01 time SNES Function norm e-03 Linear pressure_ solve converged due to CONVERGED_RTOL its 16 Linear pressure_ solve converged due to CONVERGED_RTOL its 16 Linear pressure_ solve converged due to CONVERGED_RTOL its 16 Linear solve converged due to CONVERGED_RTOL its 2 1 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL its 2 2 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL its 2 3 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL its 2 4 SNES Function norm e-04 Linear solve converged due to CONVERGED_RTOL its 2 5 SNES Function norm e-06 Linear solve converged due to CONVERGED_RTOL its 2 6 SNES Function norm e-08 Linear solve converged due to CONVERGED_RTOL its 2 7 SNES Function norm e-14 Nonlinear solve converged due to CONVERGED_FNORM_RELATIVE its 7 1 TS dt 0.01 time 0.64 Matt (Rice) PETSc MIT 56 / 69

125 Newton failure Experiments Magma Dynamics 0 TS dt 0.01 time 0.64 Time 0.64 L_2 Error: [ , , ] 0 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 2 1 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 3 2 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 5 3 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 6 4 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 7 5 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 7. 9 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 7 10 SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations SNES Function norm e-03 Linear solve converged due to CONVERGED_RTOL iterations 15. Matt (Rice) PETSc MIT 57 / 69

126 NCG+Newton options Experiments Magma Dynamics -snes_monitor -snes_converged_reason -snes_type composite -snes_composite_type multiplicative -snes_composite_sneses ncg,newtonls -sub_0_snes_monitor -sub_1_snes_monitor -sub_0_snes_type ncg -sub_0_snes_linesearch_type cp -sub_0_snes_max_it 5 -sub_1_snes_linesearch_type bt -sub_1_snes_fd_color -sub_1_snes_fd_color_use_mat -mat_coloring_type greedy -sub_1_ksp_rtol 1.0e-10 -sub_1_ksp_monitor -sub_1_ksp_gmres_restart 200 -sub_1_pc_type fieldsplit -sub_1_pc_fieldsplit_0_fields 0,2 -sub_1_pc_fieldsplit_1_fields 1 -sub_1_pc_fieldsplit_type schur -sub_1_pc_fieldsplit_schur_precondition selfp -sub_1_pc_fieldsplit_schur_factorization_type full -sub_1_fieldsplit_0_pc_type lu -sub_1_fieldsplit_pressure_ksp_rtol 1.0e-9 -sub_1_fieldsplit_pressure_pc_type gamg -sub_1_fieldsplit_pressure_ksp_gmres_restart 100 -sub_1_fieldsplit_pressure_ksp_max_it 200 Matt (Rice) PETSc MIT 58 / 69

127 Experiments NCG+Newton convergence Magma Dynamics 0 TS dt 0.01 time SNES Function norm e-03 0 SNES Function norm e-03 1 SNES Function norm e-02 2 SNES Function norm e-02 3 SNES Function norm e-02 4 SNES Function norm e-02 5 SNES Function norm e-02 0 SNES Function norm e-02 0 KSP Residual norm e+00 1 KSP Residual norm e-05 2 KSP Residual norm e-09 3 KSP Residual norm e-12 1 SNES Function norm e-03 1 SNES Function norm e-03 2 SNES Function norm e-02 3 SNES Function norm e-02 4 SNES Function norm e-03 5 SNES Function norm e-04 6 SNES Function norm e-05 7 SNES Function norm e-08 8 SNES Function norm e-13 1 TS dt 0.01 time 0.65 Matt (Rice) PETSc MIT 59 / 69

128 FAS options Experiments Magma Dynamics Top level -snes_monitor -snes_converged_reason -snes_type fas -snes_fas_type full -snes_fas_levels 4 -fas_levels_3_snes_monitor -fas_levels_3_snes_converged_reason -fas_levels_3_snes_atol 1.0e-9 -fas_levels_3_snes_max_it 2 -fas_levels_3_snes_type newtonls -fas_levels_3_snes_linesearch_type bt -fas_levels_3_snes_fd_color -fas_levels_3_snes_fd_color_use_mat -fas_levels_3_ksp_rtol 1.0e-10 -mat_coloring_type greedy -fas_levels_3_ksp_gmres_restart 50 -fas_levels_3_ksp_max_it 200 -fas_levels_3_pc_type fieldsplit -fas_levels_3_pc_fieldsplit_0_fields 0,2 -fas_levels_3_pc_fieldsplit_1_fields 1 -fas_levels_3_pc_fieldsplit_type schur -fas_levels_3_pc_fieldsplit_schur_precondition selfp -fas_levels_3_pc_fieldsplit_schur_factorization_type full -fas_levels_3_fieldsplit_0_pc_type lu -fas_levels_3_fieldsplit_pressure_ksp_rtol 1.0e-9 -fas_levels_3_fieldsplit_pressure_pc_type gamg -fas_levels_3_fieldsplit_pressure_ksp_gmres_restart 100 -fas_levels_3_fieldsplit_pressure_ksp_max_it 200 Matt (Rice) PETSc MIT 60 / 69

129 FAS options Experiments Magma Dynamics 2nd level -fas_levels_2_snes_monitor -fas_levels_2_snes_converged_reason -fas_levels_2_snes_atol 1.0e-9 -fas_levels_2_snes_max_it 2 -fas_levels_2_snes_type newtonls -fas_levels_2_snes_linesearch_type bt -fas_levels_2_snes_fd_color -fas_levels_2_snes_fd_color_use_mat -fas_levels_2_ksp_rtol 1.0e-10 -fas_levels_2_ksp_gmres_restart 50 -fas_levels_2_pc_type fieldsplit -fas_levels_2_pc_fieldsplit_0_fields 0,2 -fas_levels_2_pc_fieldsplit_1_fields 1 -fas_levels_2_pc_fieldsplit_type schur -fas_levels_2_pc_fieldsplit_schur_precondition selfp -fas_levels_2_pc_fieldsplit_schur_factorization_type full -fas_levels_2_fieldsplit_0_pc_type lu -fas_levels_2_fieldsplit_pressure_ksp_rtol 1.0e-9 -fas_levels_2_fieldsplit_pressure_pc_type gamg -fas_levels_2_fieldsplit_pressure_ksp_gmres_restart 100 -fas_levels_2_fieldsplit_pressure_ksp_max_it 200 Matt (Rice) PETSc MIT 60 / 69

130 FAS options Experiments Magma Dynamics 1st level -fas_levels_1_snes_monitor -fas_levels_1_snes_converged_reason -fas_levels_1_snes_atol 1.0e-9 -fas_levels_1_snes_type newtonls -fas_levels_1_snes_linesearch_type bt -fas_levels_1_snes_fd_color -fas_levels_1_snes_fd_color_use_mat -fas_levels_1_ksp_rtol 1.0e-10 -fas_levels_1_ksp_gmres_restart 50 -fas_levels_1_pc_type fieldsplit -fas_levels_1_pc_fieldsplit_0_fields 0,2 -fas_levels_1_pc_fieldsplit_1_fields 1 -fas_levels_1_pc_fieldsplit_type schur -fas_levels_1_pc_fieldsplit_schur_precondition selfp -fas_levels_1_pc_fieldsplit_schur_factorization_type full -fas_levels_1_fieldsplit_0_pc_type lu -fas_levels_1_fieldsplit_pressure_ksp_rtol 1.0e-9 -fas_levels_1_fieldsplit_pressure_pc_type gamg Matt (Rice) PETSc MIT 60 / 69

131 FAS options Experiments Magma Dynamics Coarse level -fas_coarse_snes_monitor -fas_coarse_snes_converged_reason -fas_coarse_snes_atol 1.0e-9 -fas_coarse_snes_type newtonls -fas_coarse_snes_linesearch_type bt -fas_coarse_snes_fd_color -fas_coarse_snes_fd_color_use_mat -fas_coarse_ksp_rtol 1.0e-10 -fas_coarse_ksp_gmres_restart 50 -fas_coarse_pc_type fieldsplit -fas_coarse_pc_fieldsplit_0_fields 0,2 -fas_coarse_pc_fieldsplit_1_fields 1 -fas_coarse_pc_fieldsplit_type schur -fas_coarse_pc_fieldsplit_schur_precondition selfp -fas_coarse_pc_fieldsplit_schur_factorization_type full -fas_coarse_fieldsplit_0_pc_type lu -fas_coarse_fieldsplit_pressure_ksp_rtol 1.0e-9 -fas_coarse_fieldsplit_pressure_pc_type gamg Matt (Rice) PETSc MIT 60 / 69