Sparse Matrix Methods • Day 1: Overview • Day 2: Direct methods • Day 3: Iterative methods • • • • • • The conjugate gradient algorithm Parallel conjugate gradient and.

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Transcript Sparse Matrix Methods • Day 1: Overview • Day 2: Direct methods • Day 3: Iterative methods • • • • • • The conjugate gradient algorithm Parallel conjugate gradient and.

Sparse Matrix Methods

• Day 1: Overview • Day 2: Direct methods • Day 3: Iterative methods • • • • • • The conjugate gradient algorithm Parallel conjugate gradient and graph partitioning Preconditioning methods and graph coloring Domain decomposition and multigrid Krylov subspace methods for other problems Complexity of iterative and direct methods

SuperLU-dist: Iterative refinement to improve solution

• • • • • • •

Iterate:

r = b – A*x backerr = max if backerr <

ε i

( r

i

/ (|A|*|x| + |b|)

i

) or backerr > lasterr/2 then stop iterating solve L*U*dx = r x = x + dx lasterr = backerr repeat Usually 0 – 3 steps are enough

(Matlab demo)

• iterative refinement

Convergence analysis of iterative refinement

Let C = I – A(LU)

-1

[ so A = (I – C)·(LU) ] r x

1 1

dx

1

= (LU)

-1

= (LU) b = b – Ax

1

= (I – A(LU)

-1

)b = Cb

-1

r

1

= (LU)

-1

Cb x

2

r

2

= x

1

+dx

1

= (LU)

-1

(I + C)b = b – Ax

2

= (I – (I – C)·(I + C))b = C

2

b . . .

In general, r

k

= b – Ax

k

= C

k

b Thus r

k

 0 if |largest eigenvalue of C| < 1.

The Landscape of Sparse Ax=b Solvers

Non symmetric Symmetric positive definite Direct A = LU

Pivoting LU

Iterative y’ = Ay

GMRES, QMR, … Cholesky Conjugate gradient More Robust More General More Robust Less Storage

D

Conjugate gradient iteration

x 0

for

= 0, r 0 = b, p k = 1, 2, 3, . . .

0 = r 0 α k = (r T k-1 r k-1 ) / (p T k-1 Ap k-1 ) step length x k = x k-1 + α k r k = r k-1 – α k p Ap k-1 k-1 approx solution residual β k = (r T k r k ) / (r T k-1 r k-1 ) improvement p k = r k + β k p k-1 search direction • • • One matrix-vector multiplication per iteration Two vector dot products per iteration Four n-vectors of working storage

Conjugate gradient: Krylov subspaces

• Eigenvalues: Au = λu { λ

1

, λ

2

, . . ., λ

n

} • Cayley-Hamilton theorem: (A – λ

1

I)·(A – λ

2

I) · · · (A – λ

n

I) = 0 Therefore

0

 Σ c

i

A

i

= 0 for some

i

n

c

i

so A

-1

=

1

 Σ (–c

i

/c

0

) A

i–1 i

n

• Krylov subspace: x  Therefore if Ax = b , then x = A

-1

b and span (b, Ab, A

2

b, . . ., A

n-1

b) = K

n

(A, b)

Conjugate gradient: Orthogonal sequences

• • • • Krylov subspace: K

i

(A, b) = span (b, Ab, A

2

b, . . ., A

i-1

b) Conjugate gradient algorithm: for i = 1, 2, 3, . . .

find x

i

 K

i

(A, b) such that r

i

= (Ax

i

– b)  K

i

(A, b) Notice r

i

 K

i+1

(A, b), so r

i

 r

j

for all j < i Similarly, the “directions” are A-orthogonal: (x

i

– x

i-1

)

T

·A· (x

j

– x

j-1

) = 0 • The magic: Short recurrences. . .

A is symmetric => can get next residual and direction from the previous one, without saving them all.

Conjugate gradient: Convergence

• In exact arithmetic, CG converges in n steps (completely unrealistic!!) • Accuracy after k steps of CG is related to: • • consider polynomials of degree k that are equal to 1 at 0.

how small can such a polynomial be at all the eigenvalues of A?

• Thus, eigenvalues close together are good.

• Condition number: κ (A) = ||A|| 2 ||A -1 || 2 = λ max (A) / λ min (A) • Residual is reduced by a constant factor by O(κ 1/2 (A)) iterations of CG.

(Matlab demo)

• • CG on grid5(15) and bcsstk08 n steps of CG on bcsstk08

Conjugate gradient: Parallel implementation

• Lay out matrix and vectors by rows • Hard part is matrix-vector product y = A*x • Algorithm Each processor j: Broadcast x(j) Compute y(j) = A(j,:)*x • May send more of x than needed • Partition / reorder matrix to reduce communication y P0 P1 P2 P3 x P0 P1 P2 P3

(Matlab demo)

• • 2-way partition of eppstein mesh 8-way dice of eppstein mesh

Preconditioners

• Suppose you had a matrix B such that: 1.

condition number κ (B -1 A) is small 2.

By = z is easy to solve • Then you could solve (B -1 A)x = B -1 b instead of Ax = b • • • • B = A is great for (1), not for (2) B = I is great for (2), not for (1) Domain-specific approximations sometimes work B = diagonal of A sometimes works • Or, bring back the direct methods technology. . .

(Matlab demo)

• bcsstk08 with diagonal precond

Incomplete Cholesky factorization (IC, ILU)

x

A R T R • Compute factors of A by Gaussian elimination, but ignore fill • Preconditioner B = R T R  A, not formed explicitly • Compute B -1 z by triangular solves (in time nnz(A)) • Total storage is O(nnz(A)), static data structure • Either symmetric (IC) or nonsymmetric (ILU)

(Matlab demo)

• bcsstk08 with ic precond

Incomplete Cholesky and ILU: Variants

• Allow one or more “levels of fill” • unpredictable storage requirements

1 2 4 3

• Allow fill whose magnitude exceeds a “drop tolerance” • may get better approximate factors than levels of fill • unpredictable storage requirements • choice of tolerance is ad hoc • Partial pivoting (for nonsymmetric A) • “Modified ILU” (MIC): Add dropped fill to diagonal of U or R • • A and R T R have same row sums good in some PDE contexts

1 2 4 3

Incomplete Cholesky and ILU: Issues

• Choice of parameters • • • good: smooth transition from iterative to direct methods bad: very ad hoc, problem-dependent tradeoff: time per iteration (more fill => more time) vs # of iterations (more fill => fewer iters) • Effectiveness • condition number usually improves (only) by constant factor (except MIC for some problems from PDEs) • still, often good when tuned for a particular class of problems • Parallelism • • Triangular solves are not very parallel Reordering for parallel triangular solve by graph coloring

(Matlab demo)

• 2-coloring of grid5(15)

Sparse approximate inverses

A B -1 • Compute B -1  A explicitly • Minimize || B -1 A – I || F (in parallel, by columns) • • • Variants: factored form of Good: very parallel B -1 , more fill, . . Bad: effectiveness varies widely

Support graph preconditioners: example

[Vaidya]

G(A) G(B) • • • • A is symmetric positive definite with negative off-diagonal nzs B is a maximum-weight spanning tree for A (with diagonal modified to preserve row sums) factor B in O(n) space and O(n) time applying the preconditioner costs O(n) time per iteration

Support graph preconditioners: example

G(A) G(B) • • • • • support each edge of A by a path in B dilation( A edge ) = length of supporting path in B congestion( B edge ) = # of supported A edges p = max congestion, q = max dilation condition number κ (B -1 A) bounded by p·q (at most O(n 2 ))

Support graph preconditioners: example

G(A) G(B) • • • can improve congestion and dilation by adding a few strategically chosen edges to B cost of factor+solve is O(n 1.75

), or O(n 1.2

) if A is planar in recent experiments [Chen & Toledo], often better than drop-tolerance MIC for 2D problems, but not for 3D.

Domain decomposition (introduction)

B 0 E A = 0 E T C F F T G • Partition the problem (e.g. the mesh) into subdomains • Use solvers for the subdomains B -1 and C -1 to precondition an iterative solver on the interface • Interface system is the Schur complement: S = G – E T B -1 E – F T C -1 F • Parallelizes naturally by subdomains

(Matlab demo)

• grid and matrix structure for overlapping 2-way partition of eppstein

Multigrid

(introduction)

• • • • • For a PDE on a fine mesh, precondition using a solution on a coarser mesh Use idea recursively on hierarchy of meshes Solves the model problem (Poisson’s eqn) in linear time!

Often useful when hierarchy of meshes can be built Hard to parallelize coarse meshes well • This is just the intuition – lots of theory and technology

Other Krylov subspace methods

• Nonsymmetric linear systems: • GMRES: for i = 1, 2, 3, . . .

find x

i

 K

i

(A, b) such that r

i

= (Ax

i

– b)  K

i

(A, b) But, no short recurrence => save old vectors => lots more space • BiCGStab, QMR, etc.: Two spaces K

i

(A, b) and K

i

(A T , b) Short recurrences => O(n) w/ mutually orthogonal bases space, but less robust • • Convergence and preconditioning more delicate than CG Active area of current research • Eigenvalues: Lanczos (symmetric), Arnoldi (nonsymmetric)

The Landscape of Sparse Ax=b Solvers

Non symmetric Symmetric positive definite Direct A = LU

Pivoting LU

Iterative y’ = Ay

GMRES, QMR, … Cholesky Conjugate gradient More Robust More General More Robust Less Storage

Complexity of direct methods

Time and space to solve any problem on any well shaped finite element mesh n

1/2

n

1/3

Space (fill): Time (flops): 2D

O(n log n) O(n

3/2

)

3D

O(n

4/3

) O(n

2

)

Complexity of linear solvers

Time to solve model problem (Poisson’s equation) on regular mesh n

1/2

Sparse Cholesky: CG, exact arithmetic: CG, no precond: CG, modified IC: CG, support trees: Multigrid: 2D O(n

1.5

) O(n

2

) O(n

1.5

) O(n

1.25

) O(n

1.20

) O(n) n

1/3

3D O(n

2

) O(n

2

) O(n

1.33

) O(n

1.17

) O(n

1.75

) O(n)