parallel programs comparisons

parallelsum.scala

import org.scalameter._
import common._
import scala.math.pow

val standardConfig = config(
  Key.exec.minWarmupRuns -> 40,
  Key.exec.maxWarmupRuns -> 80,
  Key.exec.benchRuns -> 100,
  Key.verbose -> false
) withWarmer(new Warmer.Default)

def sum1(a: Array[Int], s: Int, t: Int): Int = {
  var i= s; var sum: Int = 0
  while (i < t) {
    sum= sum + a(i)
    i= i + 1 }
  sum }

def sum2(a: Array[Int], p: Double, s: Int, t: Int): Double = {
  var i= s; var sum: Double = 0
  while (i < t) {
    sum = sum + pow(a(i): Int, p)
    i= i + 1 }
  sum }

val a = (1 to 100000).toArray
val m1 = 100000/4
val m2 = m1 + m1
val m3 = m2 + m1
val m4 = a.length

val seqtime = standardConfig measure {
  val ((sumA, sumB), (sumC, sumD)) = parallel(
    parallel(sum1(a, 0, m1), sum1(a, m1, m2)),
    parallel(sum1(a, m2, m3), sum1(a, m3, m4)))
}

val seqtime2 = standardConfig measure {
  sum1(a, 0, a.length)
}

val seqtime3 = standardConfig measure {
  a.par
    .aggregate(0)((x,y) => x + y, (x,y) => x + y)
}

val seqtime4 = standardConfig measure{
  val (r1, r2) = parallel(sum1(a,0,m2), sum1(a,m2, m4))
  r1 + r2
}

val p  = 7.35
val seqtime5 = standardConfig measure {
  val ((sumA, sumB), (sumC, sumD)) = parallel(
    parallel(sum2(a, p, 0, m1), sum2(a, p, m1, m2)),
    parallel(sum2(a, p, m2, m3), sum2(a, p, m3, m4)))
}

val seqtime6 = standardConfig measure {
  sum2(a, p, 0, a.length)
}

val seqtime7 = standardConfig measure{
  val (r1, r2) = parallel(sum2(a, p, 0,m2), sum2(a, p,m2, m4))
  r1 + r2
}

val seqtime8 = {
  a.par
    .aggregate(0.0)((x, y) => x + pow(y,p) , (x, y) => x + y)
}

When computations are less then fetching the entire array parallel programs are slower.