School of Computing Science, University of Glasgow
Abstract
Tracing JIT compilation generates units of compilation that
are easy to analyse and are known to execute frequently. The AJITPar
project aims to investigate whether the information in JIT traces can be
used to make better scheduling decisions or perform code transformations
to adapt the code for a specific parallel architecture. To achieve this goal,
a cost model must be developed to estimate the execution time of an
individual trace.
This paper presents the design and implementation of a system for extracting
JIT trace information from the Pycket JIT compiler. We define
three increasingly parametric cost models for Pycket traces. We perform
a search of the cost model parameter space using genetic algorithms to
identify the best weightings for those parameters. We test the accuracy
of these cost models for predicting the cost of individual traces on a set
of loop-based micro-benchmarks. We also compare the accuracy of the
cost models for predicting whole program execution time over the Pycket
benchmark suite. Our results show that the weighted cost model
using the weightings found from the genetic algorithm search has the
best accuracy
