matrix - Why does partitioning increase Dot execution speed?

I have noticed that multiplying a list of matrices can be significantly sped up by partitioning the list, calculating the product of the partitions matrices, and then multiplying the results.

tab = Table[RandomReal[{0,1},{8,8}],{3960}];


F=Function[{m},
    Dot @@ ((Dot @@ #)& /@ Partition[tab,m]) // AbsoluteTiming // First];

times = F /@ Divisors[3960];
ListPlot[times,PlotRange->Full]

enter image description here

In this example the computation time can be decreased by nearly two orders of magnitude by choosing the right partition size.

Can anybody explain this effect?

Edit: I think Simon Woods gave the correct explanation for numerical matrices. But the effect happens for symbolic matrices, too!

tab = Table[({{Symbol["a"<>#],Symbol["b"<>#]},{Symbol["c"<>#],Symbol["d"<>#]}})&[ToString[n]],{n,24}];

Dot @@ tab // AbsoluteTiming // First
(* 7.511497 *)

Dot @@ Dot @@@ Partition[tab,2] // AbsoluteTiming // First
(* 0.008224 *)

Answer

This happens because of unpacking when the numbers exceed $MaxMachineNumber:

fast = Dot @@@ Partition[tab, Divisors[3960][[42]]];


Developer`PackedArrayQ /@ fast
(* {True, True, True, True, True, True, True, True} *)

Max[fast] <= $MaxMachineNumber
(* True *)

slow = Dot @@@ Partition[tab, Divisors[3960][[43]]];

Developer`PackedArrayQ /@ slow

(* {False, False, False, False, False, False} *)

Max[slow] <= $MaxMachineNumber
(* False *)

The performance is best for the largest partitions which do not overflow machine arithmetic.

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