Evidence-based static branch prediction using machine learning
ACM Transactions on Programming Languages and Systems (TOPLAS)
Profile-driven instruction level parallel scheduling with application to super blocks
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Speculative hedge: regulating compile-time speculation against profile variations
Proceedings of the 29th 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
Balance scheduling: weighting branch tradeoffs in superblocks
Proceedings of the 32nd annual ACM/IEEE international symposium on Microarchitecture
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
A Machine Learning Approach to Automatic Production of Compiler Heuristics
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Adaptive java optimisation using instance-based learning
Proceedings of the 18th annual international conference on Supercomputing
Automatically constructing compiler optimization heuristics using supervised learning
Automatically constructing compiler optimization heuristics using supervised learning
Discovering Dispatching Rules Using Data Mining
Journal of Scheduling
Genetic programming applied to compiler heuristic optimization
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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Instruction scheduling is the problem of scheduling the assembly instructions output from the code generator to increase the efficiency of the final code. The instruction scheduling problem is mainly solved heuristically since finding an optimal solution requires significant computational resources and, in general, the problem of optimally scheduling instructions is known to be NP-Complete. In this paper, the specific problem of automatically creating instruction scheduling heuristics is addressed.