# Optimizing and Evaluating Transient Gradual Typing

## TL;DR

This paper analyzes the performance costs of transient gradual typing in Python, introduces static analysis to reduce overhead, and demonstrates near-zero slowdown when combined with JIT compilation.

## Contribution

It presents a static analysis and optimization that significantly reduces runtime overhead of transient gradual typing in Python implementations.

## Key findings

- Transient approach causes up to 6x slowdown in CPython.
- Static analysis reduces overhead, making partially typed programs nearly as fast as untyped.
- Combining with PyPy's JIT eliminates runtime overhead.

## Abstract

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 perform a detailed performance analysis of the transient gradual typing approach 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 Reticulated Python and the transient approach on CPython, performance decreases as programs evolve from dynamic to static types, up to a 6x 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 reduces the overall overhead to zero.