(PDF) Abid M. Malik, Tyrel Russell, Michael Chase, and Peter van Beek. Learning heuristics for basic block instruction scheduling. J. of Heuristics, 14(6):549-569, 2008. A preliminary version of the paper appears in Proceedings of the 15th CASCON, Toronto, Ontario, October, 2005.


Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block---a straight-line sequence of code with a single entry point and a single exit point---subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic. The heuristic is usually hand-crafted, a potentially time-consuming process. In contrast, we present a study on automatically learning good heuristics using techniques from machine learning. In our study, a recently proposed optimal basic block scheduler was used to generate the machine learning training data. A decision tree learning algorithm was then used to induce a simple heuristic from the training data. The automatically constructed decision tree heuristic was compared against a popular critical-path heuristic on the SPEC 2000 benchmarks. On this benchmark suite, the decision tree heuristic reduced the number of basic blocks that were not optimally scheduled by up to 55\% compared to the critical-path heuristic, and gave improved performance guarantees in terms of the worst-case factor from optimality.

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