On reordering instruction streams for pipelined computers
MICRO 22 Proceedings of the 22nd annual workshop on Microprogramming and microarchitecture
Instruction scheduling for the IBM RISC System/6000 processor
IBM Journal of Research and Development
Efficient DAG construction and heuristic calculation for instruction scheduling
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
C4.5: programs for machine learning
C4.5: programs for machine learning
Instruction scheduling in the TOBEY compiler
IBM Journal of Research and Development
MediaBench: a tool for evaluating and synthesizing multimedia and communicatons systems
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Advanced compiler design and implementation
Advanced compiler design and implementation
Learning to schedule straight-line code
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Fast Optimal Instruction Scheduling for Single-Issue Processors with Arbitrary Latencies
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Computer Architecture: A Quantitative Approach
Computer Architecture: A Quantitative Approach
An introduction to variable and feature selection
The Journal of Machine Learning Research
Optimal Superblock Scheduling Using Enumeration
Proceedings of the 37th annual IEEE/ACM International Symposium on Microarchitecture
Discovering Dispatching Rules Using Data Mining
Journal of Scheduling
Data Mining
Supervised learning linear priority dispatch rules for job-shop scheduling
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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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.