Analyzing Parallel Computing

Once again we will use image lab, this time to review Parallel Computing.

  • Change baseWidth in this line in code to increase computation requirements:-IMAGE L- For instance 320, 512, 1024, 2048, 4096.- Compare Sequential and Parallel computing code and time to achieve outputs
from IPython.display import HTML, display
from pathlib import Path  # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f
from PIL import Image as pilImage # as PIL Image is used to avoid conflicts
from io import BytesIO
import base64
import numpy as np


# prepares a series of images
def image_data(path=Path("images/"), images=None):  # path of static images is defaulted
    if images is None:  # default image
        images = [
            {'source': "Internet", 'label': "Green Square", 'file': "green-square-16.png"},
            {'source': "Peter Carolin", 'label': "Clouds Impression", 'file': "clouds-impression.png"},
            {'source': "Peter Carolin", 'label': "Lassen Volcano", 'file': "lassen-volcano.jpg"}
        ]
    for image in images:
        # File to open
        image['filename'] = path / image['file']  # file with path
    return images

# Scale to baseWidth
def scale_image(img, baseWidth):
    scalePercent = (baseWidth/float(img.size[0]))
    scaleHeight = int((float(img.size[1])*float(scalePercent)))
    scale = (baseWidth, scaleHeight)
    return img.resize(scale)

# PIL image converted to base64
def image_to_base64(img, format):
    with BytesIO() as buffer:
        img.save(buffer, format)
        return base64.b64encode(buffer.getvalue()).decode()
    
# Convert pixels to Grey Scale
def grey_pixel(pixel):
    average = (pixel[0] + pixel[1] + pixel[2]) // 3  # average pixel values and use // for integer division
    if len(pixel) > 3:
        return( (average, average, average, pixel[3]) ) # PNG format
    else:
        return( (average, average, average) )
    
# Convert pixels to Red Scale
def red_pixel(pixel):
    if len(pixel) > 3:
        return( (pixel[0], 0, 0, pixel[3]) ) # PNG format
    else:
        return( (pixel[0], 0, 0) )
    
# Convert pixels to Green Scale
def green_pixel(pixel):
    if len(pixel) > 3:
        return( (0, pixel[1], 0, pixel[3]) ) # PNG format
    else:
        return( (0, pixel[1], 0) )
    
# Convert pixels to Blue Scale
def blue_pixel(pixel):
    if len(pixel) > 3:
        return( (0, 0, pixel[2], pixel[3]) ) # PNG format
    else:
        return( (0, 0, pixel[2]) )
        
# Set Properties of Image, Scale, and convert to Base64
def image_management(image, baseWidth):  # path of static images is defaulted        
    # Image open return PIL image object
    img = pilImage.open(image['filename'])
    
    # Python Image Library operations
    image['format'] = img.format
    image['mode'] = img.mode
    image['size'] = img.size
    # Scale the Image
    img = scale_image(img, baseWidth)
    image['pil'] = img
    image['scaled_size'] = img.size
    image['numpy'] = np.array(img.getdata())
    # Scaled HTML
    image['html'] = '<img src="data:image/png;base64,%s">' % image_to_base64(image['pil'], image['format'])
    
    # Grey HTML
    # each pixel in numpy array is turned to grey 
    # then resulting list, using List Comprehension, is put back into img    
    img.putdata([grey_pixel(pixel) for pixel in image['numpy']])
    image['html_grey'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Red HTML
    img.putdata([red_pixel(pixel) for pixel in image['numpy']])
    image['html_red'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Green HTML
    img.putdata([green_pixel(pixel) for pixel in image['numpy']])
    image['html_green'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    # Blue HTML
    img.putdata([blue_pixel(pixel) for pixel in image['numpy']])
    image['html_blue'] =  '<img src="data:image/png;base64,%s">' % image_to_base64(img, image['format'])
    
    
def process_image(image, baseWidth=2048):
    image_management(image, baseWidth)
    print("---- meta data -----")
    print(image['label'])
    print(image['source'])
    print(image['format'])
    print(image['mode'])
    print("Original size: ", image['size'])
    print("Scaled size: ", image['scaled_size'])
    
    print("-- images --")
    display(HTML(image['html'])) 
    display(HTML(image['html_grey'])) 
    display(HTML(image['html_red'])) 
    display(HTML(image['html_green'])) 
    display(HTML(image['html_blue']))

Sequential Processing

The for loop iterates over the list of images and processes them one at a time, in order.

if __name__ == "__main__":
    # setup default images
    images = image_data()

    # Sequential Processing    
    for image in images:
        process_image(image)
        
    print()

Parallel Computing

In parallel or concurrent mode, the ThreadPoolExecutor is used to submit each image to a separate worker thread, allowing multiple images to be processed simultaneously. Multithreading allows multiple concurrent tasks of a process at the same time. The executor.map() method is used to apply the process_image function to each image in the images list.

  • The order in which the images are processed is not guaranteed, as threads are performed simultaneously.
import concurrent.futures

# Jupyter Notebook Visualization of Images
if __name__ == "__main__":
    # setup default images
    images = image_data()
    
    # Parallel Processsing
    # executor allocates threads, it considers core execution capability of machine
    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.map(process_image, images)  # order is not predictable
        
    print()

Observing Parallel Computing and Threads

You can observe Processes, CPU Percentage, and Threads with Tools on your machine. Common tools to monitor performance are Activity Monitor on MacOS or Task Manager on Windows.

  • This example is using top launched in VSCode Terminal.
    • PID is Process ID.
    • COMMAND is task running on machine. Python is activated when running this Jupyter notebook.
    • #TH is number of threads. This increases from 15/1 to 18/1 on my machine when running python parallel computing example.

Hacks

AP Classroom. Provide answers and thoughts on theoritical question form college board Video in section 4.3. They start at about the 9 minute mark.

  • Example 1
    • Processors cannot execute half of a process, hence processses cannot be split between two processors. This is why single core speed is still being improved after the creation of multi core systems.
    • In this example Processor 1 could take 50 seconds to complete X while Processor 2 could take 40 seconds to complete processes Y and Z.
  • Example 2
    • paralell computing will hleave processor 1 idle for 25 sceonds Data Structures. Build a List Comprehension example
  • list = [calc(item) for item in items] \/
items = ["Small car", "green plane", "AE86 Tureno", "Initial D"]
def reverse(item):
    itemList = item.split()
    return str("backwards " + itemList[1])
ReversedItems = [reverse(item) for item in items]
print(ReversedItems)