# Transforming a huge rectangular table to Sparse Array

I am interested in the most efficient way to transform a huge rectangular array to a sparse array structure.

Consider a numeric rectangular table that is rather sparse, with zero positions being set to Null rather than 0, which saves time and memory during the fill. The problem is that SparseArray function can not be directly applied to that kind of table. First, every Null should be replaced by 0, as can be seen in the following example:

Q = 100;(*the matrix dimension is QxQ.*)
f[p1_,p2_]:=If[PrimeQ[p1+p2],1,Null];(*the specific form of the matrix elements chosen for illustrative purposes only*)
mat=ParallelTable[f[p1,p2],{p1,Q},{p2,Q}];(*the matrix to be later compressed to SparseArray*)
mat0=mat/.(Null)->0;(*Here we replace Nulls with zeroes*)
sparse=SparseArray[mat0];(*Now the SparseArray may be applied*)


However, during such replacement procedure, the ByteCount[mat0] is dramatically increased, and at sufficiently large Q one runs out of memory. Besides, it does not seem logical to artificially inflate the matrix in order to be able to compress it afterwards.

So, how can I transform a huge rectangular array full of Nulls to a sparse array structure, without replacing Nulls with zeroes?

Thank you!

P.S. Other ways to form a sparse array that I am aware of are getting too speed- and memory inefficient as Q is taken large (say, Q > 50000), e.g. this way

SparseArray @ Flatten[ParallelTable[{p1, p2} -> f[p1, p2], {p1,Q}, {p2,Q}] /. (_ -> Null) -> Sequence[]];


takes 100Gb of memory to form a sparse array of 10 Gb.

P.P.S. One could ask, what's the need to compress a matrix that is already quite sparse? The SparseArray version takes less memory, anyway: ByteCount[sparse] is less than ByteCount[mat].

P.P.P.S. Above was the toy problem. The more realistic code is as follows:

<< CompiledFunctionTools
Compiler\$CCompilerOptions = {"SystemCompileOptions" ->
"-fPIC -Ofast -march=native"};
On[ Compile::noinfo]
n = 4;(*the number of electrons*)
Upp = 6;(*Upp is the upper occupied
quantum state*)
a = Subsets[Range[Upp], {n}];(*a is the set of all many-particle states*)
Q = Binomial[Upp, n];(*The number of many-particle states in a*)
(*Fast compiled function that compares two vectors and returns the positions of different elements.*)
VectorCompare =
Compile[{{v1, _Integer, 1}, {v2, _Integer, 1}}, Block[{i1 = 1, i2 = 1, d1 = InternalBag@Most[{0}],
d2 = InternalBag@Most[{0}]},
(*Run along the lists,recording differences as we go*)
While[i1 <= Length[v1] && i2 <= Length[v2],
Which[v1[[i1]] < v2[[i2]], InternalStuffBag[d1, i1]; i1++,
v1[[i1]] > v2[[i2]], InternalStuffBag[d2, i2]; i2++, True, i1++;
i2++]];
(*Fix up in case we ran off the end of one of the lists*)
While[i1 <= Length[v1], InternalStuffBag[d1, i1]; i1++];
While[i2 <= Length[v2], InternalStuffBag[d2, i2]; i2++];
{InternalBagPart[d1, All], InternalBagPart[d2, All]}],
"CompilationTarget" -> "C",
CompilationOptions -> {"ExpressionOptimization" -> True,
"InlineExternalDefinitions" -> True}, RuntimeOptions -> "Speed",
RuntimeAttributes -> {Listable}, Parallelization -> True];

CompareMatrix = SparseArray@
DeveloperToPackedArray[
ParallelTable[
If[p1 < p2, vc = VectorCompare[a[[p1]], a[[p2]]];
diff = Length@Flatten@vc;
Which[diff == 2, {vc[[1, 1]], vc[[2, 1]], 0, 0},
diff == 4, {vc[[1, 1]], vc[[2, 1]], vc[[1, 2]],
vc[[2, 2]]}]], {p1, Q}, {p2, Q}] /. (Null) -> {0, 0, 0, 0}];


The array I need could be built without any unnecessary zeros by running the following code:

CMB2 = InternalBag@Most[{0}];
CMB4 = InternalBag@Most[{0}];
Do[vc = VectorCompare[a[[p1]], a[[p2]]]; l = Length@Flatten@vc;
Which[l == 2,InternalStuffBag[CMB2, {p1, p2, vc[[1, 1]], vc[[2, 1]]}],
l == 4,InternalStuffBag[CMB4, {p1, p2, vc[[1, 1]], vc[[2, 1]], vc[[1, 2]], vc[[2, 2]]}]], {p1, 1, Q - 1}, {p2, p1 + 1, Q}];


If only it were parallelizable...

Edit: The fastest and memory-efficient solution is as follows:

CM=Join @@ ParallelMap[DeveloperToPackedArray,
Table[vc=VectorCompare[a[[p1]],a[[p2]]];diff = Length@Flatten@vc;
Which[diff > 4, empty,
diff == 4, {p1,p2,vc[[1,1]],vc[[1,2]],vc[[2,1]],vc[[2,2]]},
True, {p1,p2,vc[[1,1]],vc[[2,1]],0,0}],
{p1,1,Q-1},{p2,p1+1,Q}]];


In essence, this closes the question.

• Using a symbol like Null here instead of 0 (or 0. for a matrix of reals) is much more likely to increase memory usage ... May 16, 2014 at 19:45
• @Szabolcs Not in the case of an unpacked array it seems, at least in version 7. What do you get for the three ByteCounts in my example? May 16, 2014 at 19:48
• I don't believe ByteCount is accurate here. It's known to double count in certain cases when several parts of an expression are really a reference to the same thing in memory. ByteCount@Outer[If[PrimeQ[#1 + #2], 1] &, Range[100], Range[100]] is indeed about half the size of ByteCount@Outer[If[PrimeQ[#1 + #2], 1, 0] &, Range[100], Range[100]]. But if I use MaxMemoryUsed (introduced in v9 I think, at least for use this way) instead of ByteCount, I get the same result for both. May 16, 2014 at 19:53
• @Mr.Wizard Here's v7-compatible proof that there's no difference: screenshot pastebin May 16, 2014 at 19:56
• @Szabolcs Upon increasing the dimensions, some differences arise: screenshot May 16, 2014 at 23:08

You can specify a background for the array using the third parameter:

SparseArray[mat, Automatic, Null]

SparseArray[<2089>,{100,100},Null]


However, if you can pack the (non-sparse) array it should take less memory to use 0 than it does to use Null, assuming the rest of the array elements are machine-size integers. Example:

f2[p1_, p2_] := If[PrimeQ[p1 + p2], 1, 0];
mat2 = Table[f2[p1, p2], {p1, Q}, {p2, Q}];

mat3 = DeveloperToPackedArray[mat2];
ByteCount /@ {mat, mat2, mat3}

{117456, 244032, 40128}


If you can give your actual f function I can perhaps suggest code to build a packed array row by row. You could create a packed array with e.g. ConstantArray, then set values as needed using Part and Set. However, I shall wait for an example of your actual code before making additional recommendations.

• Specifing a background for the array is a brilliant idea, thank you! In essence, that solves my problem. As to packed arrays, I tried them of course. You see, a packed array with zeroes takes less memory then unpacked with Nulls, although SparseArray is even more compact. The real problem is that building the sparse array via the packed array inflated with zeroes takes memory larger by orders of magnitude. Please see examples in the following comment. May 16, 2014 at 20:21
• Q = 10000; f[p1_, p2_] := If[PrimeQ[p1 + p2], 1]; LaunchKernels[]; mat = ParallelTable[f[p1, p2], {p1, Q}, {p2, Q}]; // AbsoluteTiming sparse = SparseArray[mat, Automatic, Null]; ByteCount[sparse] MaxMemoryUsed[] {76.715798, Null} 175463256 1204043984 May 16, 2014 at 20:21
• Q = 10000; f2[p1_, p2_] := If[PrimeQ[p1 + p2], 1, 0]; LaunchKernels[]; mat2 = DeveloperToPackedArray[ ParallelTable[f[p1, p2], {p1, Q}, {p2, Q}]]; // AbsoluteTiming sparse2 = SparseArray[mat2]; ByteCount[sparse2] MaxMemoryUsed[] {266.039460, Null} 10400080728 17037906848 May 16, 2014 at 20:23
• @Yasha I was proposing constructing the array from packed rows, rather than packing the entire array afterward. Is this function with PrimeQ a toy example or is it actually representative of your application? May 16, 2014 at 20:28
• It is a toy example. I would like to provide the actual code but I am not sure that comment is roomy enough for this. May I add it to the original post? Besides, I have difficulties with this mini-markdown formatting in comments, sorry for that. May 16, 2014 at 20:30