I feel it would be convient if we can talk directly with the real code, so here is the code in the bottom of the post. Basically I'm trying to use LinearSolve itorately solve the Shrodinger's equation, and trying to tune the code to get some speedup.
The structure of this function is:
Do[Ct=LinearSolve[LeftMtx[t], RightMtx[t].Ct,{...}]
where LeftMtx is the compiled function HL and the RightMtx.Ct is the compiled function HRDotCt[t,CC]. My problem size is that energyLs has a length of 51, and diplMtx is a 51*51 matrix. I profiled the function and the part of the results is like this :
Calls Time Evaluation
1 11.111 TDSEPropagator[energyLs,diplMtx,testLaserParaLs,C0,ht,{211.613,240.71}];
1 11.111 TDSEPropagator[energyLs,diplMtx,testLaserParaLs,C0,ht,{211.613,240.71}]
1 11.1089 TDSEPropagator[energyLs_List,diplMtx_List,LaserParaLs_List,C0_List,dt_,solveRange_List]
1 11.1088 Module[{H0,EE,lth,Cls,tls,Ct,<<5>>},lth=Length[energyLs];H0=DiagonalMatrix[energyLs];EE=Compile[{<<1>>},Evaluate[<<1>>]];Cls=Table[0.,Evaluate[<<1>>]];tls=Range[Apply[<<2>>]];Ct=C0;<<5>>]
1 11.1055 lth=Length[energyLs];H0=DiagonalMatrix[energyLs];EE=Compile[{{<<2>>}},Evaluate[Sum[<<2>>]]];Cls=Table[0.,Evaluate[Join[<<2>>]]];tls=Range[Sequence@@Append[<<2>>]];Ct=C0;<<5>>
1 8.8298 Cls=Table[Ct=LinearSolve[HL[<<1>>],HRDotCt[<<2>>]],{t,tls}]
1 8.82979 Table[Ct=LinearSolve[HL[t],HRDotCt[t,Ct]],{t,tls}]
11748 8.77454 Ct=LinearSolve[HL[t],HRDotCt[t,Ct]]
11748 8.65778 LinearSolve[HL[t],HRDotCt[t,Ct]]
1 2.22843 Interpolation/@Transpose[Thread/@Transpose[{<<2>>}]]
11748 1.73315 HL[t]
11748 1.59476 HRDotCt[t,Ct]
1 0.452539 Transpose[Thread/@Transpose[{tls,Prepend[<<2>>]}]]
1 0.429521 Thread/@Transpose[{tls,Prepend[Drop[<<2>>],C0]}]
1 0.016248 IdenMtx=Table[If[i==j,1.,0.],{i,1,lth},{j,1,lth}]
1 0.016243 Table[If[i==j,1.,0.],{i,1,lth},{j,1,lth}]
1 0.014355 HL=Compile[{{t,Blank[<<1>>]}},IdenMtx+<<3>> Plus[<<2>>],CompilationOptions->{Rule[<<2>>],Rule[<<2>>]}]
1 0.013833 HRDotCt=Compile[{{t,Blank[<<1>>]},{CC,Blank[<<1>>],1}},(IdenMtx+Times[<<2>>]).CC,CompilationOptions->{Rule[<<2>>],Rule[<<2>>]}]
you can see that the in the total 8.77454 seconds for 11748 of itoration of
Ct=LinearSolve[HL[t],HRDotCt[t,Ct]]
it takes about 3.2s for 11748 times itoration of
HL[t] and HRDotCt[t,Ct]
which is about the 1/3 of the total time. This is where I think maybe easy to get speadup, since it has such a big performance difference compared with fortran.
You suggest to compile the whole thing, but I think LinearSolve is not compilable, and the call to the MainEvaluator may slow the compiled function (suggested
here and
here).
And I don't quite understand your second point in your last reply. Could you have a look at the code and directly refered to the code about where I would able to get some speedup? Thank you very much for your help!
Here is the code:
TDSEPropagator[energyLs_List, diplMtx_List, LaserParaLs_List, C0_List,
dt_, solveRange_List] :=
Module[{H0, EE, lth, Cls, tls, Ct, t, IdenMtx, HL, HR, HRDotCt},
lth = Length[energyLs];
H0 = DiagonalMatrix[energyLs];
EE = Compile[{{t, _Real}},
Evaluate[Sum[Block[{?, n, eFunc, tc, E0, ?0},
{?, n, eFunc, tc, E0, ?0} = LaserParaLs[[m]];
Piecewise[{{E0*eFunc[(? (t - tc))/(2 n)]*
Cos[? (t - tc) + ?0],
tc - n ?/? <= t <= tc + n ?/?}}, 0.]
], {m, 1, Length[LaserParaLs]}
]]];(*compiled piecewise function*)
Cls = Table[0., Evaluate[{t}~Join~solveRange~Join~{dt}]];
tls = Range[Sequence @@ Append[solveRange + {0., dt}, dt]];
Ct = C0;
IdenMtx =
Table[If[i == j, 1., 0.], {i, 1, lth}, {j, 1,
lth}];(*identity matrix*)
HL = Compile[{{t, _Real}},
IdenMtx + (I dt)/2 (H0 + diplMtx*EE[t + dt/2]),
CompilationOptions -> {"InlineCompiledFunctions" -> True,
"InlineExternalDefinitions" ->
True}];(*if possible I would like to speedup this part*)
HRDotCt =
Compile[{{t, _Real}, {CC, _Complex,
1}}, (IdenMtx - (I dt)/2 (H0 + diplMtx*EE[t + dt/2])).CC,
CompilationOptions -> {"InlineCompiledFunctions" -> True,
"InlineExternalDefinitions" -> True}];(*also speedup this part*)
Cls = Table[
Ct = LinearSolve[HL[t], HRDotCt[t, Ct]]
, {t, tls}];
Interpolation /@
Transpose[Thread /@ Transpose[{tls, Prepend[Drop[Cls, -1], C0]}]]
]
The input parameters for this profile are:
energyLs={0., 30.6061, 31.7552, 33.0657, 33.5432, 33.9684, 35.3902, 29.9259,30.3731, 30.5524, 30.6219, 32.8307, 32.9402, 33.0805, 33.1013, \
33.7457, 33.797, 33.8399, 33.9972, 34.0047, 34.2123, 27.4233,27.8641, 32.1649, 32.3356, 32.7108, 32.7386, 32.9177, 33.5231, \
33.6909, 33.7337, 33.7467, 33.9231, 34.2059, 34.2128, 34.3893,30.3114, 30.4774, 30.5899, 32.9143, 32.9527, 33.1013, 33.7457, \
33.7494, 33.8283, 33.8444, 33.9259, 34.0069, 34.2123, 34.2142,34.3909};
C0 = Prepend[Table[0., {Length[energyLs] - 1}], 1.];
testLaserParaLs = {{29.6265`, 5.`, Cos[#1]^2 &, 14.548390422248389`, 2.5982114443438456`*^27, -(\[Pi]/2)}, {2.37535`, 11.`,
Cos[#1]^2 &, 226.16134201858867`, 4.504198671763965`*^28, -(\[Pi]/2)}};
ht=00248;
diplMtx=Uncompress["1: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"];
and the profile function I use is (in debugger mode):
TDSEPropagator[energyLs, diplMtx, testLaserParaLs, C0, ht, {211.61295159634025`, 240.7097324408371`}]; // RuntimeTools`Profile
