Walking around outside is good for you.[citation needed] A nice amble through the trees can quiet inner turbulence and make complex engineering problems disappear.

Vicki Boykis wrote a post, Walking around the app, about a more proverbial stroll. In it, she talks about constantly using your production application’s interface to make sure the whole thing is cohesively designed with few rough edges.

She also talks about walking around other parts of the implementation of the application, fixing inconsistencies, complex machinery, and broken builds. Kind of like picking up someone else’s trash on your hike.

That’s awesome and universally good advice for pretty much every software project. It got me thinking about how I walk around the compiler.

What does your output look like?

There’s a certain class of software project that transforms data—compression libraries, compilers, search engines—for which there’s another layer of “walking around” you can do. You have the code, yes, but you also have non-trivial output.

By non-trivial, I mean an output that scales along some quality axis instead of something semi-regular like a JSON response. For compression, it’s size. For compilers, it’s generated code.

You probably already have some generated cases checked into your codebase as tests. That’s awesome. I think golden tests are fantastic for correctness and for people to help understand. But this isolated understanding may not scale to more complex examples.

How does your compiler handle, for example, switch-case statements in loops? Does it do the jump threading you expect it to? Maybe you’re sitting there idly wondering while you eat a cookie, but maybe that thought would only have occurred to you while you were scrolling through the optimizer.

An example

Say you are CF Bolz-Tereick and you are paging through PyPy IR. You notice some IR that looks like:

v0 = ...
v1 = float_abs v0
...
v2 = float_abs v1
...
v3 = float_abs v2

“Huh”, you say to yourself, “surely the optimizer can reason that the float_abs operation produces a positive number!”

But some quirk in your optimizer means that it does not. Maybe it used to work, or maybe it never did. But this little stroll revealed a bug with a one-line fix:

def return_type(op):
match op:
case float_abs(_):
-          return float
+          return float.with_range(low=0, high=None)

Now, thankfully, your IR looks much better:

v0 = ...
v1 = float_abs v0
...
...

and you can check this in as a tidy test case.

Fun fact: this was my first exposure to the PyPy project. CF walked me through fixing this bug1 live at ECOOP 2022! I had a great time.

Internal state

If checking (and, later, testing) your assumptions is tricky, this may be a sign that your library does not expose enough of its internal state to developers. This may present a usability impediment that prevents you from immediately checking your assumptions or suspicions.

For an excellent source of inspiration, see Kate’s tweets about program internals.

Even if it does provide a flag like --zjit-dump-hir to print to the console, maybe this is hard to run from a phone2 or a friend’s computer. For that, you may want friendlier tools.

Mechanical sympathy and the compiler explorer

The right kind of tool invites exploration.

Matthew Godbolt built the first friendly compiler explorer tool I used, the Compiler Explorer (“Godbolt”). It allows inputting programs into your web browser in many different languages and immediately seeing the compiled result. It will even execute your programs, within reason.

This is a powerful tool:

1. The feedback is near-instant and live updates on key-up.
2. There is no fussing with the command line and file watching.
3. Where possible, it highlights slices of source and compiled result to indicate what regions produced what output.
4. It’s open source and you can add your own compiler. This combination lowers the barrier to check things tremendously.

Now, sometimes you want the reverse: a Compiler Explorer -like thing in your terminal or editor so you don’t have to break flow. I unfortunately have not found a comparable tool.

In addition to the immediate effects of being able to spot-check certain inputs and outputs, continued use of these tools builds long-term intuition about the behavior of the compiler. It builds mechanical sympathy.

I haven’t written a lot about mechanical sympathy other than my grad school statement of purpose (PDF) and a few brief internet posts, so I will leave you with that for now.

Every function is special

Your compiler likely compiles some applications and you can likely get access to the IR for the functions in that application.

Scroll through every function’s optimized IR. If there are too many, maybe the top N functions’ IRs. See what can be improved. Maybe you will see some unexpected patterns. Even if you don’t notice anything in May, that could shift by August because of compiler advancements or a cool paper that you read in the intervening months.

One time I found a bizarre reference counting bug that was causing copy-on-write and potential memory issues by noticing that some objects that should have been marked “immortal” in the IR were actually being refcounted. The bug was not in the compiler, but far away in application setup code—and yet it was visible in the IR.

Love your tools

My conclusion is similar to Vicki’s.

Put some love into your tools. Your colleagues will notice. Your users will notice. It might even improve your mood.

Acknowledgements

Thank you to CF for feedback on the post.

The actual fix is a little more complicated since PyPy does not do float range tracking. Instead, the actual fix checks for float_abs(float_abs(x)) and rewrites to float_abs(x). ↩ 1. Just make sure to log off and touch grass. ↩