diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index cacf0ff..0090b45 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -5,8 +5,7 @@
-
-
+
@@ -160,6 +159,7 @@
+
@@ -179,7 +179,7 @@
-
+
diff --git a/ha_09/loosen_janniclas_1540907_10.py b/ha_09/loosen_janniclas_1540907_10.py
index d5746ad..35365b0 100644
--- a/ha_09/loosen_janniclas_1540907_10.py
+++ b/ha_09/loosen_janniclas_1540907_10.py
@@ -1,4 +1,5 @@
import random
+from concurrent.futures import ThreadPoolExecutor
from io import BytesIO
import numpy as np
@@ -19,33 +20,40 @@ def count_randomized_hits(iterations):
return counter
-FLAG_default = 0
-FLAG_threaded = 1
-FLAG_network = 2
-
-
def monte_carlo_methode(n, mode=0):
+ func_comm = MPI.COMM_WORLD
+ func_rank = func_comm.Get_rank()
+ func_size = func_comm.Get_size()
+
if mode == 1: # Multithreading mode
- num_threads = 16
- iterations_per_thread = n // num_threads
- with multiprocessing.Pool(num_threads) as pool:
- hits = pool.map(count_randomized_hits, [iterations_per_thread] * num_threads)
+ if func_rank == 0:
+ num_threads = 16
+ iterations_per_thread = n // num_threads
+
+ with ThreadPoolExecutor(max_workers=num_threads) as executor:
+ hits = list(executor.map(count_randomized_hits, [iterations_per_thread] * num_threads))
hit = sum(hits)
elif mode == 2: # MPI parallel mode
- comm = MPI.COMM_WORLD
- rank = comm.Get_rank()
- size = comm.Get_size()
- local_hit = count_randomized_hits(n // size)
- hit = comm.reduce(local_hit, op=MPI.SUM, root=0)
+ func_comm = MPI.COMM_WORLD
+ func_rank = func_comm.Get_rank()
+ func_size = func_comm.Get_size()
+
+ n = func_comm.bcast(n, root=0)
+
+ local_hit = count_randomized_hits(n // func_size)
+ hit = func_comm.reduce(local_hit, op=MPI.SUM, root=0)
+ hit = int(func_comm.bcast(hit, root=0))
else: # Default mode
- hit = count_randomized_hits(n)
+ if func_rank == 0:
+ hit = count_randomized_hits(n)
- pi_approx = (hit / n) * 4
- pi_diff = abs(np.pi - pi_approx)
+ if func_rank == 0:
+ pi_approx = (hit / n) * 4
+ pi_diff = abs(np.pi - pi_approx)
+ return pi_approx, pi_diff
- return pi_approx, pi_diff
def uniform_kernel(n):
@@ -65,33 +73,33 @@ def gauss_kernel(s):
return K
-FLAG_gauss = 0
-FLAG_uniform = 1
-
-
def process_image_part(data_part, kernel, padding):
y_part_size, x_part_size, _ = data_part.shape
-
- data_part_new = np.zeros((data_part.shape[0] - padding[0], data_part.shape[1] - padding[1], 3))
-
pad_y, pad_x = padding
+ data_part_new = np.zeros((y_part_size - 2 * pad_y, x_part_size - 2 * pad_x, 3))
+ # DO NOT CHANGE THIS LOOP
for i in range(pad_y, y_part_size - pad_y):
for j in range(pad_x, x_part_size - pad_x):
for k in range(3):
new_value = 0.0
- for ii in range(kernel.shape[0]):
- for jj in range(kernel.shape[1]):
- iii = ii - pad_y
- jjj = jj - pad_x
+ for ii in range(kernel.shape[1]):
+ for jj in range(kernel.shape[0]):
+ iii = ii - (kernel.shape[1] - 1) // 2
+ jjj = jj - (kernel.shape[0] - 1) // 2
new_value += kernel[ii, jj] * data_part[i + iii, j + jjj, k]
data_part_new[i - pad_y, j - pad_x, k] = new_value
return data_part_new
-def split_array(arr, n, overlap):
- sub_array_length = (len(arr) + (n - 1) * overlap) // n
- sub_arrays = [arr[i * (sub_array_length - overlap): i * (sub_array_length - overlap) + sub_array_length] for i in range(n)]
- return np.array(sub_arrays)
+def overlapping_submatrices(arr, n, overlap):
+ sub_array_length = len(arr) // n
+ # Zugegeben, den Loop hab ich auch nur durch ChatGPT hinbekommen :O
+ sub_arrays = [
+ arr[i * sub_array_length - min(i * overlap, overlap): (i + 1) * sub_array_length + min((n - i - 1) * overlap,
+ overlap)]
+ for i in range(n)
+ ]
+ return np.array(sub_arrays, dtype=object)
def process_image(img, func=0, mode=0):
@@ -102,34 +110,44 @@ def process_image(img, func=0, mode=0):
img = img.convert(mode="RGB")
data = np.asarray(img, dtype=np.float64) / 255.0
+ padding = 9
+
+ print(f"Before: {data.shape}")
if func == 1:
- kernel = uniform_kernel(7)
+ kernel = uniform_kernel(padding)
else:
- kernel = gauss_kernel(3)
+ kernel = gauss_kernel(padding)
- padding = [(kernel.shape[0] // 2), kernel.shape[1] // 2]
+ padding = (padding // 2, padding // 2)
if mode == 1: # Multithreading mode
- num_threads = 16
-
- data_parts = split_array(data, num_threads, padding[0])
-
- with multiprocessing.Pool(num_threads) as pool:
- data_new_parts = pool.starmap(process_image_part, zip(data_parts, [kernel]*num_threads, [padding]*num_threads))
+ num_threads = 5
+ data_parts = overlapping_submatrices(data, num_threads, padding[0])
+ with ThreadPoolExecutor(max_workers=num_threads) as executor:
+ data_new_parts = list(
+ executor.map(process_image_part, data_parts, [kernel] * num_threads, [padding] * num_threads))
data_new = np.concatenate(data_new_parts, axis=0)
elif mode == 2: # MPI parallel mode
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
- data_part = np.array_split(data, size, axis=0)[rank]
- data_new_part = process_image_part(data_part, kernel, padding)
- data_new_parts = comm.gather(data_new_part, root=0)
+
if rank == 0:
+ data_parts = overlapping_submatrices(data, size, padding[0])
+ else:
+ data_parts = None
+
+ data_part = comm.scatter(data_parts, root=0)
+ data_part_new = process_image_part(data_part, kernel, padding)
+
+ if rank == 0:
+ data_new_parts = comm.gather(data_part_new, root=0)
data_new = np.concatenate(data_new_parts, axis=0)
else:
data_new = None
+
data_new = comm.bcast(data_new, root=0)
else: # Default mode
@@ -137,20 +155,30 @@ def process_image(img, func=0, mode=0):
data_new = data_new * 255.0
data_new = np.uint8(data_new)
-
+ print(f"After: {data_new.shape}")
return Image.fromarray(data_new, mode="RGB")
-
if __name__ == '__main__':
- print(monte_carlo_methode(1000, FLAG_default))
- print(monte_carlo_methode(1000, FLAG_threaded))
- print(monte_carlo_methode(1000, FLAG_network))
+ comm = MPI.COMM_WORLD
+ rank = comm.Get_rank()
+ size = comm.Get_size()
+
+ FLAG_gauss = 0
+ FLAG_uniform = 1
+ FLAG_default = 0
+ FLAG_threaded = 1
+ FLAG_network = 2
+
+ if rank == 0:
+ print(monte_carlo_methode(1000, mode=FLAG_default))
+ print(monte_carlo_methode(1000, mode=FLAG_threaded))
+ print(monte_carlo_methode(1000, mode=FLAG_network))
url = "https://i.wfcdn.de/teaser/660/27020.jpg"
response = requests.get(url)
if response.status_code == 200:
image = Image.open(BytesIO(response.content))
- image = process_image(image, FLAG_uniform, FLAG_threaded)
+ image = process_image(image, FLAG_uniform, FLAG_network)
image.show()
diff --git a/hosts.txt b/hosts.txt
new file mode 100644
index 0000000..a99417a
--- /dev/null
+++ b/hosts.txt
@@ -0,0 +1,2 @@
+192.168.178.77:2
+192.168.178.73:2
\ No newline at end of file