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I always assumed that distributing a computation is faster, but it isn't necessarily true. When I do Sum[i,{i,10^4}] I retrieve an answer much faster then if I do ParallelSum[i,{i,10^4}]. Why is this the case? Is there a certain rule on when I should compute in parallel and when I should stick to a single core?

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You should try a very large data set. Every now and then the overhead in setting up a parallelization outweighs the effect of parallel processing. Using multiple CPUs is a relatively simple exercise. The situation will become worse when you use GPUs. The data transfer CPU->GPU->CPU is the critical part. –  Ernst Stelzer Jul 20 '13 at 13:42

1 Answer 1

up vote 13 down vote accepted

Sum, like Integrate, does some symbolic processing. For instance, your sum with an indefinite end point n returns a closed-form formula:

Sum[i, {i, n}]
(* 1/2 n (1 + n) *)

ParallelSum will do the actual summation, one term at a time.

There is overhead in parallelization. Often a significant bottleneck is the amount of data that has to be transferred between the master and slave kernels. Another slow-down is the time to set up the parallel computation. Neither of these is the real issue here. No matter how big I make n, I can't seem to make Sum take more than 0.1 seconds on a fresh kernel. After the first run, the symbolic result is cached and the result (for any n) is returned about 400 times faster.


To answer the second question, I do not know of either a precise or rough calculation that will tell you when parallelization results in a speed up. Consider a generic sum, where n0 below is an actual integer, such as 10000 and not a Symbol:

Sum[f[i], {i, n0}]

One consideration is how long it takes to calculate f[i]. The longer it takes, the more likely ParallelSum will be faster. Another is how big n0 is. The bigger n0 is, the more likely it is worth the time it takes to set up the parallel computation.

Examples

One way to prevent symbolic processing is to define one's own function using ?NumericQ.

Slow function

Here the computation time is simulated with Pause[0.001]. Even on a small number of terms ParallelSum is faster. (4-core/8-virtual-core 2.7GHz i7 MacBook Pro.) It's important to start with fresh kernels, since some results and parallelization set-up are cached.

Quit[]

f[i_?NumericQ] := (Pause[0.001]; i);
Table[{Sum[1. f[i], {i, 2^n}] // AbsoluteTiming // First,
   CloseKernels[]; LaunchKernels[];
   ParallelSum[1. f[i], {i, 2^n}] // AbsoluteTiming // First},
  {n, 6, 15}] // Grid

(* 0.075313  0.037028
   0.151674  0.049317
   0.299712  0.049672
   0.589223  0.111681
   1.179922  0.179192
   2.336402  0.500043
   4.795604  0.833306
   9.600580  1.740492
  19.218265  2.986417
  38.453306  5.214645 *)

Number of terms

Here it takes a fairly large number of terms before ParallelSum begins to run faster.

Quit[]

g[i_?NumericQ] := i;
Table[{Sum[1. g[i], {i, 2^n}] // AbsoluteTiming // First,
   CloseKernels[]; LaunchKernels[];
   ParallelSum[1. g[i], {i, 2^n}] // AbsoluteTiming // First},
  {n, 11, 20}] // Grid

(* 0.002350  0.032552
   0.004389  0.114484
   0.008307  0.044456
   0.016554  0.049290
   0.033395  0.064034
   0.067941  0.089265
   0.133811  0.112625
   0.275909  0.158116
   0.554793  0.407610
   1.123326  0.504677 *)

In short, I think a certain amount of testing is necessary to figure out each case precisely. For a one-time computation, it may or may not be worth the personal time it takes; instead an educated guess might be sufficient. For a program in which the computation will be done repeatedly, then it might be worth working it out.

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Thank you for your answer. I think a better example would have been Sin[0.1i] instead of 'i'. I get the same issue with sine but the parallel function does get faster when I add enough terms. The question still remains, is there a way of knowing when parallel is faster? Is testing shorter runs the only way to really determine this? –  cspirou Jul 20 '13 at 13:40
    
Actually, Sum[Sin[0.1 i], {i, n}] == 20.0083 Sin[0.05 n] Sin[0.05 (1. + 1. n)], but I'll come up with a suitable function to sum for you in my upcoming edit. (Sine can be written in terms of the exponential function, which turns the sum into geometric sums.) –  Michael E2 Jul 20 '13 at 14:05
    
The question is not if an alternative exist but if Mathematica is smart enough to do that. Even in my original example I could probably force Mathematica to avoid symbolic processing by using floating point numbers instead of integers. –  cspirou Jul 20 '13 at 14:16

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