run += 1
# Print Problem Description
print "\nAMG Scalability Study for Ax = 0, x_init = rand\n"
print "Emphasis on Robustness of Evolution Strength "
print "Measure and Root-Node Solver\n"
print "Rotated Anisotropic Diffusion in 2D"
print "Anisotropic Coefficient = %1.3e" % epsilon
print "Rotation Angle = %1.3f" % theta
# Print Tables
print_scalability(
factors_classic,
complexity_classic,
nnz_classic,
sizelist_classic,
plotting=False,
title='Classic SA\nClassic Strength Measure DropTol = %1.2f' %
classic_theta)
print_scalability(
factors_ode,
complexity_ode,
nnz_ode,
sizelist_ode,
plotting=False,
title='Classic SA\nEvolution Strength Measure DropTol = %1.2f' %
evolution_theta)
print_scalability(
factors_ode_root,
complexity_ode_root,
nnz_ode_root,
run += 1
# Print Problem Description
print "\nAMG Scalability Study for Ax = 0, x_init = rand\n"
print "Emphasis on Robustness of Evolution Strength "
print "Measure and Root-Node Solver\n"
print "Rotated Anisotropic Diffusion in 2D"
print "Anisotropic Coefficient = %1.3e" % epsilon
print "Rotation Angle = %1.3f" % theta
# Print Tables
print_scalability(
factors_classic,
complexity_classic,
nnz_classic,
sizelist_classic,
plotting=False,
title="Classic SA\nClassic Strength Measure DropTol = %1.2f" % classic_theta,
)
print_scalability(
factors_ode,
complexity_ode,
nnz_ode,
sizelist_ode,
plotting=False,
title="Classic SA\nEvolution Strength Measure DropTol = %1.2f" % evolution_theta,
)
print_scalability(
factors_ode_root,
complexity_ode_root,
nnz_ode_root,
for n in nlist:
nx = n
ny = n
print "n = %-10d of %-10d" % (n, nlist[-1])
# Rotated Anisotropic Diffusion
stencil = diffusion_stencil_2d(type='FE',
epsilon=0.001,
theta=scipy.pi / 3)
A = stencil_grid(stencil, (nx, ny), format='csr')
S = classical_strength_of_connection(A, 0.0)
numpy.random.seed(625)
x = scipy.rand(A.shape[0])
b = A * scipy.rand(A.shape[0])
ml = ruge_stuben_solver(A, max_coarse=10)
resvec = []
x = ml.solve(b, x0=x, maxiter=200, tol=1e-8, residuals=resvec)
factors[run] = (resvec[-1] / resvec[0])**(1.0 / len(resvec))
complexity[run] = ml.operator_complexity()
nnz[run] = A.nnz
sizelist[run] = A.shape[0]
run += 1
print_scalability(factors, complexity, nnz, sizelist, plotting=True)
sizelist=numpy.zeros((len(nlist),1)).ravel()
run=0
for n in nlist:
nx = n
ny = n
print "n = %-10d of %-10d"%(n,nlist[-1])
# Rotated Anisotropic Diffusion
stencil = diffusion_stencil_2d(type='FE',epsilon=0.001,theta=scipy.pi/3)
A = stencil_grid(stencil, (nx,ny), format='csr')
S = classical_strength_of_connection(A, 0.0)
numpy.random.seed(625)
x = scipy.rand(A.shape[0])
b = A*scipy.rand(A.shape[0])
ml = ruge_stuben_solver(A, max_coarse=10)
resvec = []
x = ml.solve(b, x0=x, maxiter=200, tol=1e-8, residuals=resvec)
factors[run] = (resvec[-1]/resvec[0])**(1.0/len(resvec))
complexity[run] = ml.operator_complexity()
nnz[run] = A.nnz
sizelist[run]=A.shape[0]
run +=1
print_scalability(factors, complexity, nnz, sizelist, plotting=True)
