Added monte_carlo_pi benchmark example

Author: Chaluvadi

examples/benchmarks/monte_carlo_pi.py

@@ -0,0 +1,67 @@
+#!/usr/bin/python
+
+#######################################################
+# Copyright (c) 2024, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+########################################################
+
+from random import random
+from time import time
+import arrayfire as af
+import sys
+
+try:
+    import numpy as np
+except ImportError:
+    np = None
+
+#alias range / xrange because xrange is faster than range in python2
+try:
+    frange = xrange  #Python2
+except NameError:
+    frange = range   #Python3
+
+# Having the function outside is faster than the lambda inside
+def in_circle(x, y):
+    return (x*x + y*y) < 1
+
+def calc_pi_device(samples):
+    x = af.randu((samples,))
+    y = af.randu((samples,))
+    return 4 * af.sum(in_circle(x, y)) / samples
+
+def calc_pi_numpy(samples):
+    np.random.seed(1)
+    x = np.random.rand(samples).astype(np.float32)
+    y = np.random.rand(samples).astype(np.float32)
+    return 4. * np.sum(in_circle(x, y)) / samples
+
+def calc_pi_host(samples):
+    count = sum(1 for k in frange(samples) if in_circle(random(), random()))
+    return 4 * float(count) / samples
+
+def bench(calc_pi, samples=1000000, iters=25):
+    func_name = calc_pi.__name__[8:]
+    print("Monte carlo estimate of pi on %s with %d million samples: %f" % \
+          (func_name, samples/1e6, calc_pi(samples)))
+
+    start = time()
+    for k in frange(iters):
+        calc_pi(samples)
+    end = time()
+
+    print("Average time taken: %f ms" % (1000 * (end - start) / iters))
+
+if __name__ == "__main__":
+    if (len(sys.argv) > 1):
+        af.set_device(int(sys.argv[1]))
+    af.info()
+
+    bench(calc_pi_device)
+    if np:
+        bench(calc_pi_numpy)
+    bench(calc_pi_host)