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@vascoosx
Last active October 28, 2016 02:35
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parallel programs comparisons
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)
}
@vascoosx
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vascoosx commented Oct 17, 2016

seqtime: Double = 0.17605132999999998
seqtime2: Double = 0.047938339999999975 # sequential
seqtime3: Double = 2.36307871
seqtime4: Double = 0.05351555000000002
seqtime5: Double = 4.7363107399999995
seqtime6: Double = 11.002103899999998 # sequential
seqtime7: Double = 5.541935449999999
seqtime8: Double = 6.734907823093108E40

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

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