SepehrM SepehrM - 28 days ago 12
C# Question

Parallel calculation of BigInteger factorial

As a part of my BigDecimal library, I need to calculate the factorial of any given non negative integer. So I'm using .Net 4.0 's

to be able to store huge numbers. Here is the function I'm using:

private BigInteger Factorial(BigInteger x)
BigInteger res = x;
while (x > 1)
res *= x;
return res;

It's working but not optimized. Now I want to use parallel computing, so here is what I've tried: (I have no experience with parallel programming)

public BigInteger Factorial(long x)
BigInteger res = 1;
ParallelLoopResult r = Parallel.For(2L, (x + 1), i =>
res *= i
return res;

The weird problem is that the above function works perfectly for small numbers like 5! but doesn't work for big numbers like 1000! and each time returns a completely different result. So I realized it's not thread safe and the problem is with the variable
. I wonder what the correct implementation is?

And It would be nicer if I could use BigInteger instead of long for variable


You need to make sure your parallel processes do not share any state.

For example, in the case of the factorial, I would do the following:

  • set a degree of parallelism (DOP) - usually the number of processors on your machine for compute-bound operations
  • divide the problem into independent subsets
  • process each subset in parallel
  • aggregate the results obtained from the parallel processes

This is somehow a simplified Map-Reduce.

The problem consists in multiplying together a set numbers. One way to divide this set into subsets is to use N parallel for loops where each start at a value i (where 0 < i <= N) with a step of N (and N = DOP).

Here's the code that does that:

/// <summary>
/// The max number of parallel tasks
/// </summary>
static readonly int DegreeOfParallelism = Environment.ProcessorCount;

public BigInteger Factorial(long x)
    // Make as many parallel tasks as our DOP
    // And make them operate on separate subsets of data
    var parallelTasks =
        Enumerable.Range(1, DegreeOfParallelism)
                    .Select(i => Task.Factory.StartNew(() => Multiply(x, i),

    // after all tasks are done...

    // ... take the partial results and multiply them together
    BigInteger finalResult = 1;

    foreach (var partialResult in parallelTasks.Select(t => t.Result))
        finalResult *= partialResult;

    return finalResult;

/// <summary>
/// Multiplies all the integers up to upperBound, with a step equal to DOP
/// starting from a different int
/// </summary>
/// <param name="upperBoud"></param>
/// <param name="startFrom"></param>
/// <returns></returns>
public BigInteger Multiply(long upperBound, int startFrom)
    BigInteger result = 1;

    for (var i = startFrom; i <= upperBound; i += DegreeOfParallelism)
        result *= i;

    return result;

On my machine this computes 100000! in about 30 seconds and the result is what Wolfram Alpha says it should be.


After running a few more tests, I discovered something which I didn't expect: printing out the 100000! result to the console takes ~18 seconds (the result has 456574 digits).

The results of the 100000! computation alone (without printing the number) the are:

  • ~10 seconds for parallel execution
  • ~16 seconds for sequential execution