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I want to multiply 1000by1000 matrices but MMA runs out of RAM.

LaunchKernels[]

dim = 1000;(*dimension of the matrices. There is 1000 of these 1000by1000 matrices*)

v = Developer`ToPackedArray[
Table[{
       Table[

      N@(KroneckerDelta[i, j] + Sin[i*j]) 

      , {j, 1, dim}]}
, {i, 1, dim}]];(* The matrices themselves.*)

Developer`ToPackedArray[
  ParallelSum[
              Cos[i*k]*(v[[k]]\[Transpose].v[[i]] + v[[i]]\[Transpose].v[[k]])
, {i, 1, dim}, {k, 1, dim}]];(*Multiplication part and summation*)

Here is a snapshot of task manager. I ran the code then killed the kernels:

enter image description here

How can I improve this code. The problem is MMA uses a lot of RAM. I even used packed array but it didn't help. I know that in the summation part I am summing the transpose of the multiplied matrices and I could not to do the multiplication for the second term.


I edited the question to reflect the real problem better. I used $HistoryLength = 0;, it didn't help.


Edit2

I want to see the effect of using packed arrays so I changed the dimension of the matrices and calculated the maximum used memory in the process of evaluation of those multiplications. For packed arrays I used:

v[dim_] := 
 Developer`ToPackedArray[
  Table[{Table[N@(KroneckerDelta[i, j] + Sin[i*j]), {j, 1, dim}]}, {i,
     1, dim}]];(*The matrices themselves.*)

m1 = MaxMemoryUsed[]/N[10^6]

res = Table[{MaxMemoryUsed[]/N[10^6] - m1, 
   Developer`ToPackedArray[
     Table[Cos[1.0*i*k]*(v[dim][[k]]\[Transpose].v[dim][[i]]), {i, 1, 
       dim}, {k, 1, dim}]];}, {dim, 28, 40}](* I removed the addition to the transpose  in contrast to the code provided in the above*)

axis = Table[i, {i, 28, 40}];

ListPlot[
Join[{axis}\[Transpose], {Cases[Flatten[res], _Real]}\[Transpose], 2], 
 PlotMarkers -> {\[FilledCircle], 15},
 PlotStyle -> Red, 
 Frame -> True, 
 FrameLabel -> {Style["Dimension", Bold, FontSize -> 15], Style["MaxMemoryUsed", Bold, FontSize -> 15]}, 
 FrameStyle -> Directive[Black, Bold, 20]]

Here is the result:

enter image description here

and here the result for the case I did not use packed arrays. After I got the result for packed arrays, I restart kernels.

v2[dim_] := 
  Table[{Table[N@(KroneckerDelta[i, j] + Sin[i*j]), {j, 1, dim}]}, {i,
     1, dim}];

m1 = MaxMemoryUsed[]/N[10^6]

res2 = Table[{MaxMemoryUsed[]/N[10^6] - m1, 
   Table[Cos[i*k]*(v2[dim][[k]]\[Transpose].v2[dim][[i]]), {i, 1, 
      dim}, {k, 1, dim}];}, {dim, 28, 40}]

axis = Table[i, {i, 28, 40}];

ListPlot[Join[{axis}\[Transpose], {Cases[
     Flatten[res2], _Real]}\[Transpose], 2], 
 PlotMarkers -> {\[FilledCircle], 15}, PlotStyle -> Blue, 
 Frame -> True, 
 FrameLabel -> {Style["Dimension", Bold, FontSize -> 15], 
   Style["MaxMemoryUsed", Bold, FontSize -> 15]}, 
 FrameStyle -> Directive[Black, Bold, 20]]

enter image description here

It seems using packed arrays is not memory efficient temporary. Can anybody reproduce these results?

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  • $\begingroup$ Would you not be better off with sparse arrays? $\endgroup$ – Yves Klett Nov 7 '14 at 12:14
  • $\begingroup$ I don't know about that. But because later in the calculations these matrices won't be sparse, it won't help me even if it help this particular situation. $\endgroup$ – MOON Nov 7 '14 at 12:16
  • 1
    $\begingroup$ It seems to me that t = Total[v]; u = Transpose[t].t; result = u + Transpose[u]; will give the same result in a fraction of the time. $\endgroup$ – Simon Woods Nov 7 '14 at 13:01
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    $\begingroup$ As a side note: 1) built-in matrix multiplication is already parallelized 2) CUDA acceleration for dense matrix multiplication is not very big because the speed of dense matrix multiplication depends on the size of the processor cache and CPU have a quite big cache. $\endgroup$ – ybeltukov Nov 7 '14 at 13:13
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    $\begingroup$ @molekyla777 That is not correct. SetSharedvariable does not distribute definitions, it has a completely different purpose. DistributeDefinitions is used for distributing definitions but since Mathematica 8 this happens automatically when using ParallelSum. $\endgroup$ – Szabolcs Nov 7 '14 at 14:44

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