Optimizing and Evaluating Transient Gradual Typing
Gradual typing enables programmers to combine static and dynamic typing in the same language. However, ensuring a sound interaction between the static and dynamic parts can incur significant runtime cost. In this paper, we analyze the performance of the transient design for gradual typing as implemented in Reticulated Python, a gradually typed variant of Python. The transient approach inserts lightweight checks throughout a program rather than installing proxies on higher order values. We show that, when running using CPython as a host for Reticulated Python, performance de- creases as programs evolve from dynamic to static types, up to a 6× slowdown compared to equivalent Python programs.
To reduce this overhead, we design a static analysis and optimization that removes redundant runtime checks. The optimization employs a static type inference algorithm that solves traditional subtyping constraints and also a new kind of check constraint. We evaluate the resulting performance and find that for many programs, the efficiency of partially typed programs is close to their untyped counterparts, removing most of the slowdown of transient checks. Finally, we measure the efficiency of Reticulated Python programs when running on PyPy, a tracing JIT. We find that combining PyPy with our type inference algorithm results in an average overhead of less than 1%.
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