Partial evaluation and automatic program generation
Partial evaluation and automatic program generation
Regular Approximation of Computation Paths in Logic and Functional Languages
Selected Papers from the Internaltional Seminar on Partial Evaluation
Self-tuning resource aware specialisation for prolog
PPDP '05 Proceedings of the 7th ACM SIGPLAN international conference on Principles and practice of declarative programming
Poly-controlled partial evaluation in practice
Proceedings of the 2007 ACM SIGPLAN symposium on Partial evaluation and semantics-based program manipulation
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The main goal of partial evaluation [1] is program specialization. Essentially, given a program and part of its input data|the so called static data|a partial evaluator returns a new, residual program which is specialized for the given data. An appropriate residual program for executing the remaining computations|those that depend on the so called dynamic data|is thus the output of the partial evaluator. Despite the fact that the main goal of partial evaluation is improving program efficiency (i.e., producing faster programs), there are very few approaches devoted to formally analyze the effects of partial evaluation, either a priori (prediction) or a posteriori . Recent approaches (e.g., [2,3]) have considered experimental frameworks for estimating the best division (roughly speaking, a classification of program parameters into static or dynamic), so that the optimal choice is followed when specializing the source program.