Tracing the meta-level: PyPy's tracing JIT compiler

By Carl Friedrich Bolz-Tereick, Antonio Cuni, Maciej Fijalkowski, A. Rigo. 2009

310 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning (2019, background): “Outside of DSLs, there are several projects that provide just-in-time (JIT) compilation for Python, of which Numba (Lam et al., 2015) and PyPy (Bolz et al., 2009) are two examples.”
SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python (2019, methodology): “That said, we have recently increased our efforts to support compatibility with some of these options, and our full test suite passed with the PyPy JIT compiler 67 at the 1.0 release point.”
Numba: a LLVM-based Python JIT compiler (2015, background): “Of the two main active projects today, PyPy [9] has built its own compilation infrastructure from scratch, whereas the other Pyston(7) is using LLVM.”
One VM to rule them all (2013, background): “It also generates a tracing compiler, for which the interpreter author must insert hints in form of runtime calls and annotations [8, 49].”