def thetaEuler(nx, ntmax, theta, comm):
'''theta == 0 means forward Euler method, theta == 1 means backward Euler method. 0 <= theta <= 1. '''
dx = 1.0/nx
dt = 0.5*dx**2
x = []
for i in range(nx + 1):
x.append(i*dx)
u = np.zeros(nx + 1)
dimension = nx - 1
dimension, rowA, colA, dataA = spmvParallel.sparseMatrix(dimension, theta*dt/dx**2)
dimension, rowB, colB, dataB = spmvParallel.sparseMatrix(dimension, -(1-theta)*dt/dx**2)
f = np.zeros(len(x))
for i in range(len(x)):
f[i] = F(x[i])
tol = 0.0001
iterMax = 100
step = 0
while(True):
V = []
for element in u[1:-1]:
V.append(element)
u[1:-1] = spmvcgParallel.conjugateGradient(dimension, rowA, colA, dataA, spmvParallel.productParallel(dimension, rowB, colB, dataB, u[1:-1], comm) + dt*f[1:-1], tol, iterMax, comm)
step = step + 1
residual = 0
for i in range(len(V)):
residual = residual + (V[i] - u[1:-1][i])**2
#print step
#print residual
if (step > ntmax):
break
if (np.sqrt(residual) < 0.0000001):
break
return x, u
def main():
import sys
if len(sys.argv) != 3:
print "Matrix dimension = argv[1], l = argv[2]. "
return -1
pr = cProfile.Profile()
pr.enable()
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
dimension = int(sys.argv[1])
l = float(sys.argv[2])
dimension, row, col, data = spmvParallel.sparseMatrix(dimension, l)
b = np.zeros(dimension)
for i in range(dimension):
b[i] = i + 1
tol = 0.0001
iterMax = 100
x = conjugateGradient(dimension, row, col, data, b, tol, iterMax, comm)
if False and rank == 0:
A = spmvParallel.createSparseMatrix(dimension, l)
print "Matrix A: "
print A
print "Vector b: "
print b
print "Solution of Ax = b: "
print x
print "Ax = "
print spmvParallel.productParallel(dimension, row, col, data, x, comm)
pr.disable()
pr.dump_stats("profile")
pr.print_stats()
return 0
def main():
import sys
if (len(sys.argv) != 3):
print "Matrix dimension = argv[1], l = argv[2]. "
return -1
pr = cProfile.Profile()
pr.enable()
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
dimension = int(sys.argv[1])
l = float(sys.argv[2])
dimension, row, col, data = spmvParallel.sparseMatrix(dimension, l)
b = np.zeros(dimension)
for i in range(dimension):
b[i] = i + 1
tol = 0.0001
iterMax = 100
x = conjugateGradient(dimension, row, col, data, b, tol, iterMax, comm)
if (False and rank == 0):
A = spmvParallel.createSparseMatrix(dimension, l)
print "Matrix A: "
print A
print "Vector b: "
print b
print "Solution of Ax = b: "
print x
print "Ax = "
print spmvParallel.productParallel(dimension, row, col, data, x, comm)
pr.disable()
pr.dump_stats("profile")
pr.print_stats()
return 0
def thetaEuler(nx, ntmax, theta, comm):
'''theta == 0 means forward Euler method, theta == 1 means backward Euler method. 0 <= theta <= 1. '''
dx = 1.0 / nx
dt = 0.5 * dx**2
x = []
for i in range(nx + 1):
x.append(i * dx)
u = np.zeros(nx + 1)
dimension = nx - 1
dimension, rowA, colA, dataA = spmvParallel.sparseMatrix(
dimension, theta * dt / dx**2)
dimension, rowB, colB, dataB = spmvParallel.sparseMatrix(
dimension, -(1 - theta) * dt / dx**2)
f = np.zeros(len(x))
for i in range(len(x)):
f[i] = F(x[i])
tol = 0.0001
iterMax = 100
step = 0
while (True):
V = []
for element in u[1:-1]:
V.append(element)
u[1:-1] = spmvcgParallel.conjugateGradient(
dimension, rowA, colA, dataA,
spmvParallel.productParallel(dimension, rowB, colB, dataB, u[1:-1],
comm) + dt * f[1:-1], tol, iterMax,
comm)
step = step + 1
residual = 0
for i in range(len(V)):
residual = residual + (V[i] - u[1:-1][i])**2
#print step
#print residual
if (step > ntmax):
break
if (np.sqrt(residual) < 0.0000001):
break
return x, u