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Multiple RHSs on solvers in Fortran. ~2x speed up on matlab implementation for a single RHS. for multiple RHS there are still some problems.

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Someone with some knowledge of how fortran works should look at this code.

Added a setup.py script that complies things. f2py should work on most computers, because it is included in the numpy distribution.
This commit is contained in:
Rowan Cockett
2013-11-12 10:36:20 -08:00
parent ea5dc21517
commit d3f38047e4
7 changed files with 95 additions and 409 deletions
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SimPEG/tests/test_Solver.py
+25 -25
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@@ -58,22 +58,6 @@ class TestSolver(unittest.TestCase):
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directLower_1_fortran(self):
AL = sparse.tril(self.A)
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_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={'backend':'python'})
@@ -88,9 +72,25 @@ class TestSolver(unittest.TestCase):
e = np.ones((self.M.nC,numRHS))
rhs = AL.dot(e)
x = solve.solve(rhs)
def test_directLower_1_fortran(self):
AL = sparse.tril(self.A)
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_directUpper_1(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)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directUpper_1_python(self):
AU = sparse.triu(self.A)
solve = Solver(AU, doDirect=True, flag='U', options={})
e = np.ones(self.M.nC)
@@ -98,7 +98,7 @@ class TestSolver(unittest.TestCase):
x = solve.solve(rhs)
self.assertTrue(np.linalg.norm(e-x,np.inf) < TOL, True)
def test_directUpper_M(self):
def test_directUpper_M_python(self):
AU = sparse.triu(self.A)
solve = Solver(AU, doDirect=True, flag='U', options={})
e = np.ones((self.M.nC,numRHS))
@@ -115,13 +115,13 @@ class TestSolver(unittest.TestCase):
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_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())