运行非异步功能，该功能仅在执行程序中构建大型列表？

代码中的逻辑是通过（异步）HTTP请求提取数据，然后构建大量词典，其中随机生成其中一个值：

import asyncio
import random
import string
import time

from concurrent.futures import ProcessPoolExecutor
from itertools import cycle

from httpx import AsyncClient

URL = 'http://localhost:8080'
COUNT = 1_000_000


def rand_str(length=10):
    return ''.join(random.choice(string.ascii_uppercase) for i in range(length))


def parser(data, count):
    items = []

    for _, item in zip(range(count), cycle(data)):
        item['instance'] = rand_str()
        items.append(item)

    return items


async def parser_coro(data, count):
    items = []

    for _, item in zip(range(count), cycle(data)):
        item['instance'] = rand_str()
        items.append(item)

    return items


async def run_in_executor(func, pool, *args, **kwargs):
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(pool, func, *args, **kwargs)


async def main():
    async with AsyncClient(base_url=URL) as client:
        r = await client.get('/api/alerts/')
        data = r.json()

    # Case 1
    t1 = time.perf_counter()
    parser(data, COUNT)
    t2 = time.perf_counter()
    print(f'Case 1 - sync: {t2 - t1:.3f}s')
    
    # Case 2
    t1 = time.perf_counter()
    await parser_coro(data, COUNT)
    t2 = time.perf_counter()
    print(f'Case 2 - coro (no await): {t2 - t1:.3f}s')

    # Case 3
    t1 = time.perf_counter()
    await run_in_executor(parser, None, data, COUNT)
    t2 = time.perf_counter()
    print(f'Case 3 - thread executor: {t2 - t1:.3f}s')

    # Case 4
    t1 = time.perf_counter()
    with ProcessPoolExecutor() as executor:
        await run_in_executor(parser, executor, data, COUNT)
    t2 = time.perf_counter()
    print(f'Case 4 - process executor: {t2 - t1:.3f}s')


if __name__ == '__main__':
    asyncio.run(main(), debug=True)

测试：

$ python test.py 
Case 1 - sync: 6.593s
Case 2 - coro (no await): 6.565s
Executing <Task pending name='Task-1' coro=<main() running at test.py:63> wait_for=<Future pending cb=[_chain_future.<locals>._call_check_cancel() at /root/.pyenv/versions/3.8.1/lib/python3.8/asyncio/futures.py:360, <TaskWakeupMethWrapper object at 0x7efff962a1f0>()] created at /root/.pyenv/versions/3.8.1/lib/python3.8/asyncio/base_events.py:422> cb=[_run_until_complete_cb() at /root/.pyenv/versions/3.8.1/lib/python3.8/asyncio/base_events.py:184] created at /root/.pyenv/versions/3.8.1/lib/python3.8/asyncio/base_events.py:591> took 13.176 seconds
Case 3 - thread executor: 6.675s
Case 4 - process executor: 6.726s

题：

我应该parser在执行程序中运行该函数，以便在生成列表时不阻塞主线程，否则在这种情况下将不起作用？在这种情况下，这实际上是受CPU或I / O约束的工作量吗？我猜没有任何IO，但是正在建立列表是一项CPU密集型任务，因此工作负载受CPU约束吗？