Surprises in GopherJS Performance

When the users complain that the app is 'too slow' and the devs say "we've done everything we can", I'm usually the guy who goes and finds another 30% without doing anything crazy.

Two things I see pretty consistently. First is that when there are no tall tent poles (20 functions take 5% of the time each), people don't know what to do, so they don't do anything. Second, and possibly easier to fix, is that people believe the perf analysis tool (the breakdown of where time is spent) is telling them the objective truth. Often it's wrong, which is why we try things, benchmark them, and revert changes if things get worse.

When there are no tall tent poles I switch to invocation count, which is the best secondary indicator of hotspots. There was one method that the perf tool told me was taking 10% of the run time. But the call count was fishy. Due to a bad call structure it was being called far more often than necessary. In the worst spot in the code two sequential calls were calling this function, so I flipped the code around so they could take the answer as an argument (memoization).

I reran the benchmarks. I had removed 50% of calls to a function that took 10% of our time, and the code overall was now 20% (twenty percent!) faster. Why?

Functions allocate memory. They evict cache lines in the data and instruction caches. They might even access constrained resources, like disk. And as you said, they change how the JIT decides to optimize things.

Sometimes, the symptom is that the code that runs immediately afterward gets blamed for problems they didn't create, and the profiler has no way of following the problem back to the root cause, so it assigns blame at the point of contention, not at the start of the contention.

The only tools I've found that helps with this are clean coding practices, and figuring out if your invocation counts match your expectations (I will run a call tree 100 times and then compare the call counts of everything to find things that were called 2+ times as often as they strictly should have been called)

Yes, v8 and all other modern JS engines perform function inlining and type profiling. Still, it is extremely common to see modern JS engines be competitive with C on small microbenchmarks, but lag behind on large, realistic codebases. There are several reasons for this:

1. JS engines must make tradeoffs between what to inline and what not - each inlining requires a recompilation. Offline C compilers don't have such concerns.

2. Inlining creates large functions, and large codebases tend to have large functions anyhow, and large functions take longer to JIT, making realistic codebases much more challenging for JS engines.

3. It's fairly easy for modern JS engines to figure out types at runtime in a small loop. However, when figuring out types in a large program spread over many functions, it takes substantial overhead to try to do a holistic solution, and instead, JS engines generally just do a local analysis and hope that what really needs to be optimized is inlined anyhow - but see 1 and 2. And when not inlining, function call arguments generally do not happen in an optimized type, but in boxed form.

Overall, it is not surprising at all that the article saw C-like performance on a small micro-benchmark, on a modern browser. But in a realistic codebase, there almost certainly would be a large slowdown. That's why asm.js exists and why WebAssembly is on the way.

>> low-level C implementation compiled with -O3, the max optimization setting.

IME, -O2 always comes out faster than -O3. In this case, though, the difference is fairly insignificant.

Sometimes, I think Amdahl's Law is the most useful thing I learned about during my CS degree.

> When there are no tall tent poles I switch to invocation count

Oh, yes! It's so useful to keep an eye on invocation counts. For example, one "problematic" service in a team I managed was being analyzed by an engineer on my team. He was focused on % time culprits, but I spied a highly suspicious entry a fair ways down the list taking many, many orders of magnitude more invocations than anything else. Off I went to have a look...

It turns out the original dev had meant to put a blocking call in in the code, but had gotten mixed up and used a polling version instead. (Both were non-obviously named and signature interchangeable. Sigh.) The baseline CPU hit in the perf run wasn't significant, but in context of this service, it produced a very severe performance cliff characteristic under high site load.

A one-line change corrected that error, and suddenly the operations team stopped talking about that service (with evil glares ;-) at every opportunity.