Linear Solvers

# Krylov Solvers and Algebraic Multigrid with hypre

Demonstrate utility of multigrid

## At a Glance

Why multigrid over a Krylov solver for large problems? | Understand multigrid concept. | Faster convergence, better scalability. |

Why use more aggressive coarsening for AMG? | Understand need for low complexities. | Lower memory use, faster times, but more iterations. |

Why a structured solver for a structured problem? | Understand importance of suitable data structures | Higher efficiency, faster solve times. |

## The Problem Being Solved

We consider the Poisson equation \[-\Delta u = f\]

on a cuboid of size \(n_x \times n_y \times n_z\) with Dirichlet boundary conditions \(u = 0\).

It is discretized using central finite differences, leading to a symmetric positive matrix.

**Note:** To begin this lesson…

```
cd track-5-numerical/krylov_amg_hypre
```

## The Example Source Code

For the first part of the hands-on lessons we will use the executable ij. Various solver, problem and parameter options can be invoked by adding them to the command line. A complete set of options will be printed by typing

```
./ij -help
```

Here is an excerpt of the output of this command with all the options relevant for the hands-on lessons.

```
Usage: ij [<options>]
Choice of Problem:
-laplacian [<options>] : build 7pt 3D laplacian problem (default)
-difconv [<opts>] : build convection-diffusion problem
-n <nx> <ny> <nz> : problem size per process
-P <Px> <Py> <Pz> : process topology
-a <ax> : convection coefficient
-rotate [<opts>] : build 2D problem with rotated anisotropy
-eps <eps> : anisotropy for rotated problem
-alpha <alpha> : angle by which anisotropy is rotated
Choice of solver:
-amg : AMG only
-amgpcg : AMG-PCG
-pcg : diagonally scaled PCG
-amggmres : AMG-GMRES with restart k (default k=10)
-gmres : diagonally scaled GMRES(k) (default k=10)
-amgbicgstab : AMG-BiCGSTAB
-bicgstab : diagonally scaled BiCGSTAB
-k <val> : dimension Krylov space for GMRES
.....
-tol <val> : set solver convergence tolerance = val
-max_iter <val> : set max iterations
-agg_nl <val> : set number of aggressive coarsening levels (default:0)
-iout <val> : set output flag
0=no output 1=matrix stats
2=cycle stats 3=matrix & cycle stats
-print : print out the system
```

## Running the Example

### First Set of Runs (Krylov Solvers)

Run the first example for a small problem of size 27000 using restarted GMRES with a Krylov space of size 10.

```
./ij -n 30 30 30 -gmres
```

#### Expected Behavior/Output

You should get something that looks like this

```
Running with these driver parameters:
solver ID = 4
(nx, ny, nz) = (30, 30, 30)
(Px, Py, Pz) = (1, 1, 1)
(cx, cy, cz) = (1.000000, 1.000000, 1.000000)
Problem size = (30 x 30 x 30)
=============================================
Generate Matrix:
=============================================
Spatial Operator:
wall clock time = 0.000000 seconds
wall MFLOPS = 0.000000
cpu clock time = 0.000000 seconds
cpu MFLOPS = 0.000000
RHS vector has unit components
Initial guess is 0
=============================================
IJ Vector Setup:
=============================================
RHS and Initial Guess:
wall clock time = 0.000000 seconds
wall MFLOPS = 0.000000
cpu clock time = 0.000000 seconds
cpu MFLOPS = 0.000000
Solver: DS-GMRES
HYPRE_GMRESGetPrecond got good precond
=============================================
Setup phase times:
=============================================
GMRES Setup:
wall clock time = 0.000000 seconds
wall MFLOPS = 0.000000
cpu clock time = 0.000000 seconds
cpu MFLOPS = 0.000000
=============================================
Solve phase times:
=============================================
GMRES Solve:
wall clock time = 0.270000 seconds
wall MFLOPS = 0.000000
cpu clock time = 0.270000 seconds
cpu MFLOPS = 0.000000
GMRES Iterations = 392
Final GMRES Relative Residual Norm = 9.915663e-09
Total time = 0.270000
```

Note the total time and the number of iterations. Now increase the Krylov subspace by changing input to -k to 40, and finally 75.

Number of iterations and times generally improve except for the last run, which is somewhat slower because the last iterations are more expensive. Iterations: 392, 116, 73. Times: 0.27, 0.16, 0.17.

None, since the number of iterations is 73. Here full GMRES was used.

Now solve this problem using -pcg and -bicgstab.

Conjugate gradient takes 74 iterations and 0.04 seconds, BiCGSTAB 51 iterations and 0.05 seconds. Conjugate gradient has the lowest time, but BiCGSTAB has the lowest number of iterations.

It requires two matrix vector operations and additional vector operations per iteration, and thus each iteration takes longer than an iteration of PCG.

We now consider the diffusion-convection equation \[-\Delta u + a \nabla \cdot u = f\]

on a cuboid with Dirichlet boundary conditions.

The diffusion part is discretized using central finite differences, and upwind finite differences are used for the advection term. For \(a = 0\) we just get the Poisson equation, but when \(a > 0\) we get a nonsymmetric linear system.

Now let us apply Krylov solvers to the convection-diffusion equation with \(a=10\), starting with conjugate gradient.

```
./ij -n 50 50 50 -difconv -a 10 -pcg
```

PCG fails, because the linear system is nonsymmetric.

Now try GMRES(20) and BiCGSTAB.

```
./ij -n 50 50 50 -difconv -a 10 -gmres -k 20
```

```
./ij -n 50 50 50 -difconv -a 10 -bicgstab
```

BiCGSTAB and GMRES both solve the problem. BiCGSTAB is faster than GMRES(20) for this problem.

Now let us scale up the Poisson problem starting with a cube of size \(50 \times 50 \times 50\) on one process:

```
mpiexec -n 1 ./ij -n 50 50 50 -pcg -P 1 1 1
```

Now we increase the problem size to a cube of size \(100 \times 100 \times 100\) by increasing the number of processes to 8 using the process topology -P 2 2 2.

```
mpiexec -n 8 ./ij -n 50 50 50 -pcg -P 2 2 2
```

the number of iterations increases from 124 to 249, the time from 0.55 seconds to 1.46 seconds.

### Second Set of Runs (Algebraic Multigrid)

Now perform the previous weak scaling study using algebraic multigrid starting with

```
mpiexec -n 1 ./ij -n 50 50 50 -amg -P 1 1 1
```

followed by

```
mpiexec -n 8 ./ij -n 50 50 50 -amg -P 2 2 2
```

AMG solves the problem using significantly less iterations, and time increases somewhat slower. Number of iterations: 12, 23. Total time: 0.51, 1.18 seconds.

Now repeat the scaling study using AMG as a preconditioner for CG:

```
mpiexec -n 1 ./ij -n 50 50 50 -amgpcg -P 1 1 1
```

```
mpiexec -n 8 ./ij -n 50 50 50 -amgpcg -P 2 2 2
```

Using PCG preconditioned with AMG further decreases the number of iterations and solve times. Number of iterations: 8, 11. Total time: 0.47, 0.89 seconds.

Now let us take a look at the complexities of the last run by printing some setup statistics:

```
mpiexec -n 8 ./ij -n 50 50 50 -amgpcg -P 2 2 2 -iout 1
```

You should now see the following statistics:

```
HYPRE_ParCSRPCGGetPrecond got good precond
Num MPI tasks = 8
Num OpenMP threads = 1
BoomerAMG SETUP PARAMETERS:
Max levels = 25
Num levels = 8
Strength Threshold = 0.250000
Interpolation Truncation Factor = 0.000000
Maximum Row Sum Threshold for Dependency Weakening = 1.000000
Coarsening Type = HMIS
measures are determined locally
No global partition option chosen.
Interpolation = extended+i interpolation
Operator Matrix Information:
nonzero entries per row row sums
lev rows entries sparse min max avg min max
===================================================================
0 1000000 6940000 0.000 4 7 6.9 0.000e+00 3.000e+00
1 499594 8430438 0.000 7 42 16.9 -2.581e-15 4.000e+00
2 113588 5267884 0.000 18 83 46.4 -9.556e-15 5.515e+00
3 14088 1099948 0.006 16 126 78.1 -2.339e-14 8.187e+00
4 2585 235511 0.035 11 183 91.1 -9.932e-14 1.622e+01
5 366 25888 0.193 11 181 70.7 2.032e-01 4.293e+01
6 44 1228 0.634 14 44 27.9 9.754e+00 1.501e+02
7 9 77 0.951 7 9 8.6 1.198e+01 3.267e+02
Interpolation Matrix Information:
entries/row min max row sums
lev rows cols min max weight weight min max
=================================================================
0 1000000 x 499594 1 4 1.429e-01 4.545e-01 5.000e-01 1.000e+00
1 499594 x 113588 1 4 1.330e-02 5.971e-01 2.164e-01 1.000e+00
2 113588 x 14088 1 4 -1.414e-02 5.907e-01 5.709e-02 1.000e+00
3 14088 x 2585 1 4 -4.890e-01 6.377e-01 2.236e-02 1.000e+00
4 2585 x 366 1 4 -1.185e+01 5.049e+00 8.739e-03 1.000e+00
5 366 x 44 1 4 -2.597e+00 3.480e+00 6.453e-03 1.000e+00
6 44 x 9 1 4 -2.160e-01 8.605e-01 -6.059e-02 1.000e+00
Complexity: grid = 1.630274
operator = 3.170169
memory = 3.837342
BoomerAMG SOLVER PARAMETERS:
Maximum number of cycles: 1
Stopping Tolerance: 0.000000e+00
Cycle type (1 = V, 2 = W, etc.): 1
Relaxation Parameters:
Visiting Grid: down up coarse
Number of sweeps: 1 1 1
Type 0=Jac, 3=hGS, 6=hSGS, 9=GE: 13 14 9
Point types, partial sweeps (1=C, -1=F):
Pre-CG relaxation (down): 0
Post-CG relaxation (up): 0
Coarsest grid: 0
```

This output contains some statistics for the AMG preconditioner. It shows the number of levels, the average number of nonzeros in total and per row for each matrix \(A_i\) as well as each interpolation operator \(P_i\). It also shows the operator complexity, which is defined as the sum of the number of nonzeroes of all operators \(A_i\) divided by the number of nonzeroes of the original matrix \(A\): \(\frac{\sum_i^L {nnz(A_i)}}{nnz(A)}\). It also gives the memory complexity, which is defined by \(\frac{\sum_i^L {nnz(A_i + P_i)}}{nnz(A)}\).

It increases significantly through level 4 and decreases after that. It is much larger than the original level.

It is caused by the Galerkin product, i.e. the product of the three matrices R, A, and P.

No, we would prefer a number that is closer to 1.

Now, let us see what happens if we coarsen more aggressively on the finest level:

```
mpiexec -n 8 ./ij -n 50 50 50 -amgpcg -P 2 2 2 -iout 1 -agg_nl 1
```

We now receive the following output for average number of nonzeroes and complexities:

```
Operator Matrix Information:
nonzero entries per row row sums
lev rows entries sparse min max avg min max
===================================================================
0 1000000 6940000 0.000 4 7 6.9 0.000e+00 3.000e+00
1 79110 1427282 0.000 6 33 18.0 -1.779e-14 8.805e+00
2 16777 817577 0.003 12 91 48.7 -2.059e-14 1.589e+01
3 2235 153557 0.031 19 132 68.7 6.580e-14 3.505e+01
4 309 18445 0.193 17 160 59.7 1.255e+00 8.454e+01
5 50 1530 0.612 13 50 30.6 1.521e+01 3.237e+02
6 5 25 1.000 5 5 5.0 6.338e+01 3.572e+02
Interpolation Matrix Information:
entries/row min max row sums
lev rows cols min max weight weight min max
=================================================================
0 1000000 x 79110 1 9 2.646e-02 9.722e-01 2.778e-01 1.000e+00
1 79110 x 16777 1 4 7.709e-03 1.000e+00 2.709e-01 1.000e+00
2 16777 x 2235 1 4 2.289e-03 7.928e-01 5.909e-02 1.000e+00
3 2235 x 309 1 4 -6.673e-02 5.759e-01 4.594e-02 1.000e+00
4 309 x 50 1 4 -6.269e-01 3.959e-01 2.948e-02 1.000e+00
5 50 x 5 1 4 -1.443e-01 1.083e-01 -4.496e-02 1.000e+00
Complexity: grid = 1.098486
operator = 1.348475
memory = 1.700654
```

As you can see, the number of levels, the number of nonzeroes per rows and the complexities have decreased.

The number of iterations increases (17 vs. 11), but total time is less (0.62 vs 0.89)

Now let us use aggressive coarsening in the first two levels.

```
mpiexec -n 8 ./ij -n 50 50 50 -amgpcg -P 2 2 2 -iout 1 -agg_nl 2
```

Complexities decrease further to 1.22, but the number of iterations is increasing to 26 and total time increases as well. Choosing to aggressively coarsen on the second level does not lead to further time savings, but gives further memory savings. If achieving the shortest time is the objective, coarsen aggressively on the second level is not adviced.

So far, we achieved the best overall time to solve a Poisson problem on a cube of size \(100 \times 100 \times\) using conjugate gradient preconditioned with AMG with one level of aggressive coarsening.

How would a structured solver perform on this problem? We now use the driver for the structured interface, which will also give various input options by typing

```
./struct -help
```

To run the structured solver PFMG for this problem type

```
mpiexec -n 8 ./struct -n 50 50 50 -P 2 2 2 -pfmg
```

The number of iterations 35, but the total time is less (0.40)

Now run it as a preconditioner for conjugate gradient.

```
mpiexec -n 8 ./struct -n 50 50 50 -pfmgpcg -P 2 2 2
```

The number of iterations 14, but the total time is less (0.20)

To get even better total time, now run the non-Galerkin version.

```
mpiexec -n 8 ./struct -n 50 50 50 -pfmgpcg -P 2 2 2 -rap 1
```

The number of iterations remains 14, but the total time is less (0.18)

### Additional Exercises

We will now consider a two-dimensional problem with a rotated anisotropy on a rectangular domain. Let us begin with a grid-aligned anisotropy.

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 0 -pcg -iout 3
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 0 -gmres -k 100 -iout 3
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 0 -bicgstab -iout 3
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 0 -amg -iout 3
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 0 -pfmg
```

The residual norms for all solvers improve, but only AMG and PFMG converge within less than 1000 iterations.

Now let us rotate the anisotropy by 45 degrees.

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 45 -amg
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 45 -amgpcg
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 45 -amggmres
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 45 -pfmg
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 45 -pfmgpcg
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 45 -pfmggmres
```

The order from slowest to fastest is: PFMG, PFMG-GMRES, PFMG-CG, AMG, AMG-GMRES, AMG-CG. PFMG does not work well for non-grid-aligned anisotropies, but convergence improves when PFMG is combined with a Krylov solver. AMG can handle non-grid-aligned anisotropies well.

Now let us rotate the anisotropy by 30 degrees.

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 30 -amg
```

```
./ij -rotate -n 300 300 -eps 0.01 -alpha 30 -amgpcg
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 30 -pfmg
```

```
./struct -rotate -n 300 300 -eps 0.01 -alpha 30 -pfmgpcg
```

The order from slowest to fastest is: PFMG, AMG, AMG-CG, PFMG-CG. While AMG is signifcantly better than PFMG, this problem is harder for it than the previous problem. PFMG-CG is faster here than AMG-CG.

Let us now scale up the problem for AMG-CG and PFMG-CG.

```
mpiexec -n 2 ./ij -P 2 1 -rotate -n 300 300 -eps 0.01 -alpha 30 -amgpcg
```

```
mpiexec -n 4 ./ij -P 2 2 -rotate -n 300 300 -eps 0.01 -alpha 30 -amgpcg
```

```
mpiexec -n 8 ./ij -P 4 2 -rotate -n 300 300 -eps 0.01 -alpha 30 -amgpcg
```

```
mpiexec -n 2 ./struct -P 2 1 1 -rotate -n 300 300 -eps 0.01 -alpha 30 -pfmgpcg
```

```
mpiexec -n 4 ./struct -P 2 2 1 -rotate -n 300 300 -eps 0.01 -alpha 30 -pfmgpcg
```

```
mpiexec -n 8 ./struct -P 4 2 1 -rotate -n 300 300 -eps 0.01 -alpha 30 -pfmgpcg
```

Both solvers scale well, with PFMG-CG taking more iterations, but overall less time than AMG-CG.

We now consider the diffusion-convection equation \[-\Delta u + a \nabla \cdot u = f\]

on a cuboid with Dirichlet boundary conditions.

The diffusion part is discretized using central finite differences, and upwind finite differences are used for the advection term. For \(a = 0\) we just get the Poisson equation, but when \(a > 0\) we get a nonsymmetric linear system.

Now let us apply Krylov solvers to the convection-diffusion equation with \(a=10\), starting with conjugate gradient.

```
./ij -n 50 50 50 -difconv -a 10 -pcg
```

PCG fails, because the linear system is nonsymmetric.

Now try GMRES(20), BiCGSTAB, and AMG with and without aggressive coarsening.

```
./ij -n 50 50 50 -difconv -a 10 -gmres -k 20
```

```
./ij -n 50 50 50 -difconv -a 10 -bicgstab
```

```
./ij -n 50 50 50 -difconv -a 10 -amg
```

```
./ij -n 50 50 50 -difconv -a 10 -amg -agg_nl 1
```

BiCGSTAB, GMRES and AMG with or without aggressive coarsening solve the problem. The order slowest to fastest for this problem is: GMRES(20), AMG, BiCGSTAB, AMG with aggressive coarsening.

Let us solve the problem using structured multigrid solvers.

```
./struct -n 50 50 50 -a 10 -pfmg
```

```
./struct -n 50 50 50 -a 10 -pfmg -rap 1
```

```
./struct -n 50 50 50 -a 10 -pfmggmres
```

```
./struct -n 50 50 50 -a 10 -pfmggmres -rap 1
```

The non-Galerkin version of PFMG as alone solver fails. The order from largest to least number of iterations is: Non-Galerkin PFMG-GMRES, PFMG, PFMG-GMRES. But PFMG alone solves the problem faster.

## Out-Brief

We experimented with several Krylov solvers, GMRES, conjugate gradient and BiCGSTAB, and observed the effect of increasing the size of the Krylov space for restarted GMRES. We investigated why multigrid methods are preferable over generic solvers like conjugate gradient for large suitable PDE problems. Additional improvements can be achieved when using them as preconditioners for Krylov solvers like conjugate gradient. For unstructured multigrid solvers, it is important to keep complexities low, since large complexities lead to slow solve times and require much memory. For structured problems, solvers that take advantage of the structure of the problem are more efficient than unstructured solvers.

### Further Reading

To learn more about algebraic multigrid, see An Introduction to Algebraic Multigrid

More information on hypre , including documentation and further publications, can be found here