AI Enhanced HW Performance Modeling using ChatGPT

I was informed by a colleague who is well-versed in performance modeling that Python offers powerful packages for creating performance models with minimal code and the added benefit of C++ bindings for increased speed. I was eager to test this out and decided to use ChatGPT to assist me with the task of creating a SimPy module based code for a producer-consumer problem. "create performance model in simpy for producer consumer problem"

To my satisfaction, the code provided by ChatGPT was concise, elegant and easy to understand. I ran the code in a Spyder window and encountered a small error related to a global variable, which I was able to quickly fix.

Fixed code below:

import simpy

def producer(env, buffer):

??for i in range(15):

????yield env.timeout(1)

????print(f'Produced item {i} at {env.now}')

????buffer.put(i)

def consumer(env, buffer):

??while True:

????j = yield buffer.get()

????print(f'Consumed item {j} at {env.now}')

????yield env.timeout(2)

env = simpy.Environment()

buffer = simpy.Store(env)

env.process(producer(env, buffer))

env.process(consumer(env, buffer))

env.run(until=15)

What next: Create a performance model for 3 microprocessors talking to the same memory controller using simpy - Try it out and comment here how it went.