benchmark representing async-heavy, heterogeneous workload

Open
#239 7 comments 3 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
performance

Research direction

Start by reviewing the existing async benchmarks in pyperformance and compare their coroutine behavior with the heterogeneous, context-switch-heavy workload described here. Define a representative benchmark and establish how it should be run and measured; done means the workload is added to the suite and produces comparable interpreter performance results.

Written by the indexing model from the issue text.

Description

context: Recently I set about trying to find a faster Python interpreter for my async app, which has a high rate of context switching, many coroutines, and each coroutine running different code. I found that the app runs 2x slower on PyPy, and 20% faster on Pyston-full. One reason may be that a tracing JIT will naively trace across context switches, such that a given trace will never be repeated, due to arbitrary occurrence and ordering of coroutine resumes.

There really seems to be nothing in the benchmark world that represents this kind of async workload-- which, by the way, I expect to become more popular with time. The current async benchmarks in pyperformance have nothing like this-- the coroutines are trivial and homogenous.

It's concerning that the Pyston full fork is being retired, while PyPy blindly continues as if everything's OK, and faster-python proceeds at a furious pace-- all without evaluating a workload that is significant today, and may become more so in the future.

Dominant language
Python
Stars
1k
Forks
203
Avg merge
1h 20m
Merged PRs (30d)
2

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from python/pyperformance

All issues in python/pyperformance

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.