Performance of scala parallel collection processing

Question

I have scenarios where I will need to process thousands of records at a time. Sometime, it might be in hundreds, may be upto 30000 records. I was thinking of using the scala's parallel collection. So just to understand the difference, I wrote a simple pgm like below:

object Test extends App{
  val list = (1 to 100000).toList
  Util.seqMap(list)
  Util.parMap(list)
}

object Util{
  def seqMap(list:List[Int]) = {
    val start = System.currentTimeMillis
    list.map(x => x + 1).toList.sum
    val end = System.currentTimeMillis
    println("time taken =" + (end - start))
    end - start
  }
  def parMap(list:List[Int]) = {
    val start = System.currentTimeMillis
    list.par.map(x => x + 1).toList.sum
    val end = System.currentTimeMillis
    println("time taken=" + (end - start))
    end - start
  }
}

I expected that running in parallel will be faster. However, the output I was getting was

time taken =32
time taken=127

machine config :

Intel i7 processor with 8 cores
16GB RAM
64bit Windows 8

What am I doing wrong? Is this not a correct scenario for parallel mapping?

Answer 1

The issue is that the operation you are performing is so fast (just adding two ints) that the overhead of doing the parallelization is more than the benefit. The parallelization only really makes sense if the operations are slower.

Think of it this way: if you had 8 friends and you gave each one an integer on a piece of paper and told them to add one, write the result down, and give it back to you, which you would record before giving them the next integer, you'd spend so much time passing messages back and forth that you could have just done all the adding yourself faster.

ALSO: Never do .par on a List because the parallelization procedure has to copy the entire list into a parallel collection and then copy the whole thing back out. If you use a Vector, then it doesn't have to do this extra work.