From c4167681d7c4057cf82254ff81abc5310708f18d Mon Sep 17 00:00:00 2001
From: NunoSempere <nuno.sempere@protonmail.com>
Date: Sun, 25 Feb 2024 19:18:16 -0300
Subject: [PATCH] add concurrency

---
 probppl.go | 41 ++++++++++++++++++++++++++++-------------
 1 file changed, 28 insertions(+), 13 deletions(-)

diff --git a/probppl.go b/probppl.go
index 1cec82c..801874b 100644
--- a/probppl.go
+++ b/probppl.go
@@ -5,6 +5,7 @@ import (
 	"git.nunosempere.com/NunoSempere/probppl/choose"
 	"math"
 	rand "math/rand/v2"
+	"sync"
 )
 
 type src = *rand.Rand
@@ -119,7 +120,7 @@ func draw148PplFromDistributionAndCheck(d IntProbs, r src, show bool) int64 {
 
 func getUnnormalizedBayesianUpdateForDistribution(d IntProbs, r src) int64 {
 	var sum int64 = 0
-	n := 10_000
+	n := 1000
 	for i := 0; i < n; i++ {
 		/* if i%1000 == 0 {
 			fmt.Println(i)
@@ -133,22 +134,36 @@ func getUnnormalizedBayesianUpdateForDistribution(d IntProbs, r src) int64 {
 
 func main() {
 
-	var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
-
-	n_dists := 10
+	n_dists := 1000
 	var dists = make([]IntProbsWeights, n_dists)
 
-	for i := 0; i < n_dists; i++ {
-		people_known_distribution := generatePeopleKnownDistribution(r)
-		result := getUnnormalizedBayesianUpdateForDistribution(people_known_distribution, r)
-		if i%10 == 0 {
-			fmt.Printf("%d/%d\n", i, n_dists)
-		}
-		if result > 0 {
-			dists[i] = IntProbsWeights{IntProbs: people_known_distribution, w: result}
-		}
+	// Prepare for concurrency
+	num_threads := 8
+	var wg sync.WaitGroup
+	wg.Add(num_threads)
+
+	for i := range num_threads {
+		go func() {
+			defer wg.Done()
+			var r = rand.New(rand.NewPCG(uint64(i), uint64(i+1)))
+			for j := i * (n_dists / num_threads); j < (i+1)*(n_dists/num_threads); j++ {
+				people_known_distribution := generatePeopleKnownDistribution(r)
+				result := getUnnormalizedBayesianUpdateForDistribution(people_known_distribution, r)
+				/*
+					if i%10 == 0 {
+						fmt.Printf("%d/%d\n", i, n_dists)
+					}
+				*/
+				if result > 0 {
+					dists[j] = IntProbsWeights{IntProbs: people_known_distribution, w: result}
+				}
+			}
+
+		}()
+
 	}
 
+	wg.Wait()
 	// Now calculate the posterior
 	sum_weights := int64(0)
 	for _, dist := range dists {