Meta-tracing JIT compilers can be applied to a variety of differ- ent languages without explicitly encoding language semantics into the compiler. So far, they lacked a way to give the language im- plementor control over runtime feedback. This restricted their per- formance. In this paper we describe the mechanisms in PyPy’s meta-tracing JIT that can be used to control runtime feedback in language-specific ways. These mechanisms are flexible enough to express classical VM techniques such as maps and runtime type feedback.

The performance of many dynamic language implementations suffers from high allocation rates and runtime type checks. This makes dynamic languages less applicable to purely algorithmic problems, despite their growing popularity. In this paper we present a simple compiler optimization based on online partial evaluation to remove object allocations and runtime type checks in the context of a tracing JIT. We evaluate the optimization using a Python VM and find that it gives good results for all our (real-life) benchmarks.

We attempt to apply the technique of Tracing JIT Compilers in the context of the PyPy project, i.e. to programs that are interpreters for some dynamic languages, including Python. Tracing JIT compilers can greatly speed up programs that spend most of their time in loops in which they take similar code paths. However, applying an unmodified tracing JIT to a program that is itself a bytecode interpreter results in very limited or no speedup. In this paper we show how to guide tracing JIT compilers to greatly improve the speed of bytecode interpreters. One crucial point is to unroll the bytecode dispatch loop, based on two hints provided by the implementer of the bytecode interpreter. We evaluate our technique by applying it to two PyPy interpreters: one is a small example, and the other one is the full Python interpreter.