Efficiently computing static single assignment form and the control dependence graph
ACM Transactions on Programming Languages and Systems (TOPLAS)
BURG: fast optimal instruction selection and tree parsing
ACM SIGPLAN Notices
PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
Beyond induction variables: detecting and classifying sequences using a demand-driven SSA form
ACM Transactions on Programming Languages and Systems (TOPLAS)
Instruction selection using binate covering for code size optimization
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
A new approach to the minimum cut problem
Journal of the ACM (JACM)
Optimal code selection in DAGs
Proceedings of the 26th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
Optimal Code Generation for Expression Trees
Journal of the ACM (JACM)
Graph-based code selection techniques for embedded processors
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Fast and flexible instruction selection with on-demand tree-parsing automata
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Code generation for STA architecture
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Generalized instruction selection using SSA-graphs
Proceedings of the 2008 ACM SIGPLAN-SIGBED conference on Languages, compilers, and tools for embedded systems
mTags: augmenting microkernel messages with lightweight metadata
ACM SIGOPS Operating Systems Review
Intermediate representations in imperative compilers: A survey
ACM Computing Surveys (CSUR)
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Instruction selection is a compiler optimisation that translates the intermediate representation of a program into a lower intermediate representation or an assembler program. We use the SSA form as an intermediate representation for instruction selection. Patterns are used for translation and are expressed as production rules in a graph grammar. The instruction selector seeks for a syntax derivation with minimal costs optimising execution time, code size, or a combination of both. Production rules are either base rules which match nodes in the SSA graph or chain rules which convert results of operations. We present a new algorithm for placing chain rules in a control flow graph. This new algorithm places chain rules optimally for an arbitrary cost metric. Experiments with the MiBench and SPEC2000 benchmark suites show that our proposed algorithm is feasible and always yields better results than simple strategies currently in use. We reduce the costs for placing chain rules by 25% for the MiBench suite and by 11% for the SPEC2000 suite.