An improvement to bottom-up tree pattern matching
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Optimal code generation for expression trees: an application BURS theory
POPL '88 Proceedings of the 15th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
BEG: a generator for efficient back ends
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
Efficient retargetable code generation using bottom-up tree pattern matching
Computer Languages
Incremental Generation of Parsers
IEEE Transactions on Software Engineering
Hard-coding bottom-up code generation tables to save time and space
Software—Practice & Experience
A retargetable compiler for ANSI C
ACM SIGPLAN Notices
BURG: fast optimal instruction selection and tree parsing
ACM SIGPLAN Notices
Engineering a simple, efficient code-generator generator
ACM Letters on Programming Languages and Systems (LOPLAS)
ACM Transactions on Programming Languages and Systems (TOPLAS)
Fast out-of-order processor simulation using memoization
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
A Retargetable C Compiler: Design and Implementation
A Retargetable C Compiler: Design and Implementation
Combining stack caching with dynamic superinstructions
Proceedings of the 2004 workshop on Interpreters, virtual machines and emulators
Effective compiler generation by architecture description
Proceedings of the 2006 ACM SIGPLAN/SIGBED conference on Language, compilers, and tool support for embedded systems
Optimal chain rule placement for instruction selection based on SSA graphs
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
Generalized instruction selection using SSA-graphs
Proceedings of the 2008 ACM SIGPLAN-SIGBED conference on Languages, compilers, and tools for embedded systems
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Tree parsing as supported by code generator generators like BEG, burg, iburg, lburg and ml-burg is a popular instruction selection method. There are two existing approaches for implementing tree parsing: dynamic programming, and tree-parsing automata; each approach has its advantages and disadvantages. We propose a new implementation approach that combines the advantages of both existing approaches: we start out with dynamic programming at compile time, but at every step we generate a state for a tree-parsing automaton, which is used the next time a tree matching the state is found, turning the instruction selector into a fast tree-parsing automaton. We have implemented this approach in the Gforth code generator. The implementation required little effort and reduced the startup time of Gforth by up to a factor of 2.5.