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Fortran Solvers. Faster than Matlab!!

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Need to implement multiple RHSs.
This commit is contained in:
Rowan Cockett
2013-11-10 17:38:23 -08:00
parent 059ccd40b3
commit ea5dc21517
3 changed files with 136 additions and 19 deletions
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SimPEG/tests/test_Solver.py
+37 -4
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@@ -58,17 +58,33 @@ class TestSolver(unittest.TestCase):
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directLower_1(self):
def test_directLower_1_fortran(self):
AL = sparse.tril(self.A)
solve = Solver(AL, doDirect=True, flag='L', options={})
solve = Solver(AL, doDirect=True, flag='L', options={'backend':'fortran'})
e = np.ones(self.M.nC)
rhs = AL.dot(e)
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directLower_M(self):
# def test_directLower_M_fortran(self):
# AL = sparse.tril(self.A)
# solve = Solver(AL, doDirect=True, flag='L', options={'backend':'fortran'})
# e = np.ones((self.M.nC,numRHS))
# rhs = AL.dot(e)
# x = solve.solve(rhs)
# self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directLower_1_python(self):
AL = sparse.tril(self.A)
solve = Solver(AL, doDirect=True, flag='L', options={})
solve = Solver(AL, doDirect=True, flag='L', options={'backend':'python'})
e = np.ones(self.M.nC)
rhs = AL.dot(e)
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directLower_M_python(self):
AL = sparse.tril(self.A)
solve = Solver(AL, doDirect=True, flag='L', options={'backend':'python'})
e = np.ones((self.M.nC,numRHS))
rhs = AL.dot(e)
x = solve.solve(rhs)
@@ -90,6 +106,23 @@ class TestSolver(unittest.TestCase):
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directUpper_1_fortran(self):
AU = sparse.triu(self.A)
solve = Solver(AU, doDirect=True, flag='U', options={'backend':'fortran'})
e = np.ones(self.M.nC)
rhs = AU.dot(e)
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
# def test_directUpper_M_fortran(self):
# AU = sparse.triu(self.A)
# solve = Solver(AU, doDirect=True, flag='U', options={'backend':'fortran'})
# e = np.ones((self.M.nC,numRHS))
# rhs = AU.dot(e)
# x = solve.solve(rhs)
# self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directDiagonal_1(self):
AD = sdiag(self.A.diagonal())
solve = Solver(AD, doDirect=True, flag='D', options={})