benchmark representing async-heavy, heterogeneous workload

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.

首先檢視 pyperformance 中現有的非同步基準測試，並將它們的協程行為與此處描述的異質、頻繁進行內容切換的工作負載進行比較。定義一個具代表性的基準測試，並確立應如何執行及測量它；當該工作負載加入套件並產生可比較的直譯器效能結果時，即表示完成。