add: monte carlo parallelism example

30-concurrency/xxx-monte-carlo/main.go

@@ -0,0 +1,80 @@
+// For more tutorials: https://blog.learngoprogramming.com
+//
+// Copyright © 2018 Inanc Gumus
+// Learn Go Programming Course
+// License: https://creativecommons.org/licenses/by-nc-sa/4.0/
+//
+
+package main
+
+import (
+	"fmt"
+	"math"
+	"math/rand"
+	"os"
+	"runtime"
+	"strconv"
+	"time"
+)
+
+func main() {
+	if len(os.Args) != 2 {
+		exit("Please provide the sample rate")
+	}
+
+	samples, err := strconv.Atoi(os.Args[1])
+	if err != nil {
+		exit("Incorrect sample rate")
+	}
+
+	elapse := measure(time.Now())
+	pi := spread(samples, runtime.NumCPU())
+
+	fmt.Println("PI  : ", pi)
+	fmt.Println("Time: ", elapse())
+}
+
+func exit(msg string) {
+	fmt.Fprintln(os.Stderr, msg)
+	os.Exit(1)
+}
+
+func measure(start time.Time) func() time.Duration {
+	return func() time.Duration {
+		return time.Since(start)
+	}
+}
+
+// spread the work to P number of estimate go funcs.
+// returns the estimation.
+func spread(samples int, P int) (estimated float64) {
+	counts := make(chan float64)
+
+	for i := 0; i < P; i++ {
+		go func() { counts <- estimate(samples / P) }()
+	}
+
+	for i := 0; i < P; i++ {
+		estimated += <-counts
+	}
+	return estimated / float64(P)
+}
+
+func estimate(N int) float64 {
+	const radius = 1.0
+
+	var (
+		seed   = rand.NewSource(time.Now().UnixNano())
+		random = rand.New(seed)
+		inside int
+	)
+
+	for i := 0; i < N; i++ {
+		x, y := random.Float64(), random.Float64()
+
+		if num := math.Sqrt(x*x + y*y); num < radius {
+			inside++
+		}
+	}
+	return 4 * float64(inside) / float64(N)
+}