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

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.

SimPEG/utils/TriSolve.f

@@ -1,54 +1,64 @@
 c File TriSolve.f
-       subroutine forward(al, ial, jal, b, nv, n,  x)
+       subroutine forward(al, ial, jal, b, nv, n, nRHS, x)
        double precision al(nv)
        integer ial(n+1)
        integer jal(nv)
-       double precision b(n)
-       double precision x(n)
+       double precision b(n,nRHS)
+       double precision x(n,nRHS)
        integer nv
        integer n
+       integer nRHS
+       integer rhs
 cf2py  intent(in) :: al
 cf2py  intent(in) :: ial
 cf2py  intent(in) :: jal
 cf2py  intent(in) :: b
 cf2py  intent(in) :: nv
 cf2py  intent(in) :: n
+cf2py  intent(in) :: nRHS
 cf2py  intent(out) :: x
        real ( kind = 8 ) t
 
-       do k = 1, n
-          t = b(k)
-          do j = ial(k)+1, ial(k+1)
-             t = t - al(j) * x(jal(j)+1)
+       do rhs = 1, nRHS
+          do k = 1, n
+             t = b(k,rhs)
+             do j = ial(k)+1, ial(k+1)
+                t = t - al(j) * x(jal(j)+1,rhs)
+             end do
+             x(k,rhs) = t/al(ial(k+1))
           end do
-          x(k) = t/al(ial(k+1))
        end do
        end subroutine forward
 
 
-       subroutine backward(au,iau, jau, b, nv, n, x)
+       subroutine backward(au,iau, jau, b, nv, n, nRHS, x)
        double precision au(nv)
        integer iau(n+1)
        integer jau(nv)
-       double precision b(n)
-       double precision x(n)
+       double precision b(n,nRHS)
+       double precision x(n,nRHS)
        integer nv
        integer n
+       integer nRHS
+       integer rhs
 cf2py  intent(in) :: au
 cf2py  intent(in) :: iau
 cf2py  intent(in) :: jau
 cf2py  intent(in) :: b
 cf2py  intent(in) :: nv
 cf2py  intent(in) :: n
+cf2py  intent(in) :: nRHS
 cf2py  intent(out) :: x
        real ( kind = 8 ) t
 
-       do k = n, 1, -1
-          t = b(k)
-          do j = iau(k)+1, iau(k+1)
-             t = t - au(j) * x(jau(j)+1)
+       do rhs = 1, nRHS
+          do k = n, 1, -1
+             t = b(k,rhs)
+             do j = iau(k)+1, iau(k+1)
+                t = t - au(j) * x(jau(j)+1,rhs)
+             end do
+             x(k,rhs) = t/au(iau(k)+1)
           end do
-          x(k) = t/au(iau(k)+1)
        end do
 
        end subroutine backward