While gradual typing has proven itself attractive to programmers, many systems have avoided sound gradual typing due to the run time overhead of enforcement. In the context of sound gradual typing, both anecdotal and systematic evidence has suggested that run time costs are quite high, and often unacceptable, casting doubt on the viability of soundness as an approach. We show that these overheads are not fundamental, and that with appropriate improvements, just-in-time compilers can greatly reduce the overhead of sound gradual typing . Our study takes benchmarks published in a recent paper on gradual typing performance in Typed Racket (Takikawa et al., POPL 2016) and evaluates them using a experimental tracing JIT compiler for Racket, called Pycket. On typical benchmarks, Pycket is able to eliminate more than 90% of the gradual typing overhead. While our current results are not the final word in optimizing gradual typing, we show that the situation is not dire, and where more work is needed. Pycket's performance comes from several sources, which we detail and measure individually. First, we apply a sophisticated tracing JIT compiler and optimizer, automatically generated in Pycket using the RPython framework originally created for PyPy. Second, we focus our optimization efforts on the challenges posed by run time checks, implemented in Racket by chaperones and impersonators . We introduce representation improvements, including a novel use of hidden classes to optimize these data structures.

We present Pycket, a high-performance tracing JIT compiler for Racket. Pycket supports a wide variety of the sophisticated features in Racket such as contracts, continuations, classes, structures, dynamic binding, and more. On average, over a standard suite of benchmarks, Pycket outperforms existing compilers, both Racket's JIT and other highly-optimizing Scheme compilers. Further, Pycket provides much better performance for Racket proxies than existing systems, dramatically reducing the overhead of contracts and gradual typing. We validate this claim with performance evaluation on multiple existing benchmark suites. The Pycket implementation is of independent interest as an application of the RPython meta-tracing framework (originally created for PyPy), which automatically generates tracing JIT compilers from interpreters. Prior work on meta-tracing focuses on bytecode interpreters, whereas Pycket is a high-level interpreter based on the CEK abstract machine and operates directly on abstract syntax trees. Pycket supports proper tail calls and first-class continuations. In the setting of a functional language, where recursion and higher-order functions are more prevalent than explicit loops, the most significant performance challenge for a tracing JIT is identifying which control flows constitute a loop---we discuss two strategies for identifying loops and measure their impact.