Describe the bug
Running with coveragepy on PyPy drastically slows down programs using Decimal:

~/tmp/pypyperftest$ time poetry run coverage run -m covperftest.covperftest 1000000010000000real7m37,942suser7m36,118ssys0m1,348s~/tmp/pypyperftest$ time poetry run python -m covperftest.covperftest 1000000010000000real0m5,891suser0m5,267ssys0m0,606s

I've noticed this issue while experimenting with PyPy's newest versions in the past, trying to migrate to Python 3.8. At the time I didn't pay much attention, just gave up. Now that PyPy launched a new version supporting 3.9, I tried to investigate a bit more and got the results above. At first I thought that maybe it was because PyPy is using Decimal with a pure Python implementation, but forcing the C implementation yields similar results (if not slower).

To Reproduce

1. What version of Python are you using?
   • Pypy 3.9 (7.3.8) - but I've noticed this problem while using earlier versions of PyPy.
2. What version of coverage.py shows the problem?
   • I tested with the latest release, version 6.3.2.
3. What versions of what packages do you have installed?
   • I tried to test in a virtualenv with as few packages as possible:

cffi==1.15.0coverage==6.3.2covperftest==1.0.0greenlet==0.4.13hpy==0.0.3readline==6.2.4.1tomli==2.0.1

1. What code shows the problem?
   • I'm not sure where the problem in coveragepy is (or if there's even a problem at all, maybe it's something related to how Decimal works on PyPy, but I thought it would be better to report this here).
2. What commands did you run?
   • I created a sample script to test this, which I executed in a virtualenv managed using poetry:

import randomimport sysfrom decimal import Decimalsize = int(sys.argv[1]) if len(sys.argv) > 1 else 100000values = []for _ in range(size):    values.append(Decimal(3) * random.randint(0, 10000))print(len(values), file=sys.stderr)

Expected behavior
Although a worse performance is to be expected when getting the coverage, it gets much worse when using Decimal. I tried the same code using just an integer, and the execution time was around 4.5x slower when using coveragepy. But when using Decimal, it was 77x slower.