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Warmup improvements: more efficient trace representation

Hello everyone.

I'm pleased to inform that we've finished another round of improvements to the warmup performance of PyPy. Before I go into details, I'll recap the achievements that we've done since we've started working on the warmup performance. I picked a random PyPy from November 2014 (which is definitely before we started the warmup work) and compared it with a recent one, after 5.0. The exact revisions are respectively ffce4c795283 and cfbb442ae368. First let's compare pure warmup benchmarks that can be found in our benchmarking suite. Out of those, pypy-graph-alloc-removal numbers should be taken with a grain of salt, since other work could have influenced the results. The rest of the benchmarks mentioned is bottlenecked purely by warmup times.

You can see how much your program spends in warmup running PYPYLOG=jit-summary:- pypy under "tracing" and "backend" fields (in the first three lines). An example looks like that:

[e00c145a41] {jit-summary
Tracing:        71      0.053645 <- time spent tracing & optimizing
Backend:        71      0.028659 <- time spent compiling to assembler
TOTAL:                  0.252217 <- total run time of the program

The results of the benchmarks

benchmark time - old time - new speedup JIT time - old JIT time - new
function_call 1.86 1.42 1.3x 1.12s 0.57s
function_call2 5.17s 2.73s 1.9x 4.2s 1.6s
bridges 2.77s 2.07s 1.3x 1.5s 0.8s
pypy-graph-alloc-removal 2.06s 1.65s 1.25x 1.25s 0.79s

As we can see, the overall warmup benchmarks got up to 90% faster with JIT time dropping by up to 2.5x. We have more optimizations in the pipeline, with an idea how to transfer some of the JIT gains into more of a total program runtime by jitting earlier and more eagerly.

Details of the last round of optimizations

Now the nitty gritty details - what did we actually do? I covered a lot of warmup improvements in the past blog posts so I'm going to focus on the last change, the jit-leaner-frontend branch. This last change is simple, instead of using pointers to store the "operations" objects created during tracing, we use a compact list of 16-bit integers (with 16bit pointers in between). On 64bit machine the memory wins are tremendous - the new representation is 4x more efficient to use 16bit pointers than full 64bit pointers. Additionally, the smaller representation has much better cache behavior and much less pointer chasing in memory. It also has a better defined lifespan, so we don't need to bother tracking them by the GC, which also saves quite a bit of time.

The change sounds simple, but the details in the underlaying data mean that everything in the JIT had to be changed which took quite a bit of effort :-)

Going into the future on the JIT front, we have an exciting set of optimizations, ranging from faster loops through faster warmup to using better code generation techniques and broadening the kind of program that PyPy speeds up. Stay tuned for the updates.

We would like to thank our commercial partners for making all of this possible. The work has been performed by baroquesoftware and would not be possible without support from people using PyPy in production. If your company uses PyPy and want it to do more or does not use PyPy but has performance problems with the Python installation, feel free to get in touch with me, trust me using PyPy ends up being a lot cheaper than rewriting everything in go :-)

Best regards,
Maciej Fijalkowski


Peter wrote on 2016-04-08 08:55:

It would be nice to compare speed with C-Python and on short benchmarks, as that is where warmup time matters the most

Maciej Fijalkowski wrote on 2016-04-08 09:14:

Those benchmarks are very synthetic warmup-oriented ones. It means you exec() piece of code and then run it 2000 times and then exec again. Any other short-running programs have a lot more noise where you have multiple effects taking place and it would be really hard to compare between old and new pypy. That said it's a fair requirement, we have one more branch in the pipeline and I'll try to get more real world data.