Poly-controlled partial evaluation in practice

Abstract

Poly-Controlled Partial Evaluation (PCPE) is a powerful approach to partial evaluation, which has recently been proposed. PCPE takes into account sets of control strategies instead of a single one. Thus, different control strategies can be assigned to different call patterns, possibly obtaining results that cannot be obtained using a single control strategy. PCPE can be implemented as a searchbased algorithm, producing sets of candidate optimized programs. The quality of each of these programs is assessed through the use of a fitness function, which can be resource aware, in the sense that it can take multiple factors into account, such as run-time and code size. Unfortunately, PCPE suffers from an inherent blowup of its search space when implemented as an all-solutions, searchbased algorithm. Thus, in order to use it in practice we must be able to prune its search space without losing the (most) interesting solutions. In this work we explore several techniques for pruning the search space of PCPE. Some of these techniques are based on heuristics, while others are based on branch and bound and are guaranteed to obtain an optimal solution. Our experimental results show that, when combined with the proposed pruning techniques, PCPE can cope with realistic programs. Also, that the solutions obtained by PCPE outperform the solutions found by PE under similar conditions.

Profiling for Run-Time Checking of Computational Properties and Performance Debugging in Logic Programs

“…Thus, the time needed for getting the value of such attribute was directly proportional to the number of times that the attribute was modified. As mentioned before, our profiler has also been successfully applied to resource-aware poly-controlled partial evaluation [15]. This technique combines different control strategies to obtain optimizations that cannot be obtained using a single control technique.…”

“…Besides the already mentioned applications to run-time checking of computational properties and in performance debugging, our profiler implementation has also been successfully applied to program optimization and, in particular, to resource-aware poly-controlled partial evaluation [15]. This technique combines different control strategies to obtain optimizations that cannot be obtained using a single control technique.…”

Oracle-Based Partial Evaluation

“…This generate+evaluate approach to PCPE [13] has been implemented and tested on several benchmarks, showing that PCPE can obtain better specialized programs than traditional partial evaluation. Unfortunately, this approach to PCPE is too costly in practice, even with the techniques for reducing the search space studied in [11], since the number of states in the search space grows exponentially with the cardinality of CS.…”

“…Thus, PCPE can produce residual programs that are not directly achievable by traditional partial evaluation using any of the considered control strategies in isolation. This often results in hybrid solutions with better fitness value than any of the solutions achievable by traditional PE, for a number of different resource-aware fitness functions [11].…”

Trace Analysis for Predicting the Effectiveness of Partial Evaluation

“…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., [1,5]) 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.…”