Add matmul benchmark for vala

matmul/build.sh

@@ -14,6 +14,7 @@ javac matmul.java
 kotlinc matmul.kt -include-runtime -d matmul-kt.jar
 mcs -debug- -optimize+ matmul.cs
 dotnet build -c Release
+valac -X -O3 -o matmul_vala matmul.vala
 
 # numpy for matrix mult in python
 # brew install numpy

matmul/matmul.vala

@@ -0,0 +1,45 @@
+void main (string[] args) {
+	int n = 100;
+	if (args.length > 1) {
+		n = int.parse(args[1]);
+	}
+	n = n / 2 * 2;
+	double[,] a = matgen(n);
+	double[,] b = matgen(n);
+	double[,] x = matmul(a, b);
+	print("%f\n", x[n/2,n/2]);
+}
+
+double[,] matgen (int n) {
+	double[,] arr = new double[n,n];
+	double tmp = 1.0 / n / n;
+	for (int i = 0; i < n; ++i) {
+		for (int j = 0; j < n; ++j) {
+			arr[i,j] = tmp * (i - j) * (i + j);
+		}
+	}
+	return arr;
+}
+
+double[,] matmul (double[,] a, double[,] b) {
+	int m = a.length[0];
+	int n = a.length[1];
+	int p = b.length[1];
+	double[,] x = new double[m,p];
+	double[,] c = new double[p,n];
+	for (int i = 0; i < n; ++i) { // transpose
+		for (int j = 0; j < p; ++j) {
+			c[j,i] = b[i,j];
+		}
+	}
+	for (int i = 0; i < m; ++i) {
+		for (int j = 0; j < p; ++j) {
+			double s = 0.0;
+			for (int k = 0; k < n; ++k) {
+				s += a[i,k] * c[j,k];
+			}
+			x[i,j] = s;
+		}
+	}
+	return x;
+}

matmul/run.sh

@@ -56,3 +56,5 @@ echo Tcl
 ../xtime.rb tclsh matmul.tcl 1500
 echo Swift
 ../xtime.rb swift -O matmul.swift 1500
+echo Vala
+../xtime.rb ./matmul_vala 1500