Test and analyze the data transfer capabilities of computer systems. Stress test computer networks, memory, storage and ports.

Data Performance was developed to assist users in evaluating and optimizing data transfer performance on their computer systems. It provides step-by-step guidance in developing Python programs that simulate and analyze bulk data transfers, helping users to accurately test and measure the capabilities of their systems. By guiding users through the entire process, from setting up tests to interpreting results, it ensures that the evaluations are thorough and reflective of real-world scenarios.

Once the data transfer tests are complete, the GPT aids in analyzing the results, offering insights into the performance metrics and identifying potential bottlenecks in the system. This analysis helps users understand how their system handles large data transfers and what areas might require optimization. The GPT's ability to break down complex performance data into actionable information is crucial for users who want to make informed decisions about their system’s data transfer capabilities.

In addition to providing technical guidance, this GPT also suggests optimization techniques to enhance data transfer performance. By analyzing test results and considering factors like network bandwidth, storage speed, and CPU usage, it offers tailored recommendations for improving system efficiency. Whether users are working on a personal computer or a large-scale enterprise system, this GPT empowers them to effectively test and optimize data transfer performance across various platforms.

Example Windows Load Test

Here's an example code block for testing local disk-to-disk transfer performance on Windows using Python. This example will copy a 1 GB file from the C: drive to the D: drive and measure the time taken for the transfer.

import os
import shutil
import time

# Define test parameters
file_size = 1 * 1024 * 1024 * 1024  # 1 GB
number_of_files = 1
source_drive = "C:\\"
destination_drive = "D:\\"
test_file_name = "test_file.bin"

# Create a large test file on the source drive
source_file_path = os.path.join(source_drive, test_file_name)
with open(source_file_path, "wb") as f:
    f.write(os.urandom(file_size))

# Measure transfer time for each file
total_time = 0
for i in range(number_of_files):
    start_time = time.time()
    destination_file_path = os.path.join(destination_drive, f"{test_file_name}_{i}")
    shutil.copy(source_file_path, destination_file_path)
    transfer_time = time.time() - start_time
    total_time += transfer_time
    print(f"File {i+1} transfer time: {transfer_time:.2f} seconds")

# Calculate and print the average transfer time
average_transfer_time = total_time / number_of_files
print(f"Average transfer time: {average_transfer_time:.2f} seconds")

# Clean up: remove the test files
os.remove(source_file_path)
for i in range(number_of_files):
    os.remove(os.path.join(destination_drive, f"{test_file_name}_{i}"))

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