Transformation As Search

In model-driven engineering, model transformations are con- sidered a key element to generate and maintain consistency between re- lated models. Rule-based approaches have become a mature technology and are widely used in different application domains. However, in var- ious scenarios, these solutions still suffer from a number of limitations that stem from their injective and deterministic nature. This article pro- poses an original approach, based on non-deterministic constraint-based search engines, to define and execute bidirectional model transforma- tions and synchronizations from single specifications. Since these solely rely on basic existing modeling concepts, it does not require the intro- duction of a dedicated language. We first describe and formally define this model operation, called transformation as search, then describe a proof-of-concept implementation and discuss experiments on a reference use case in software engineering

Search-Based Model Transformations

Huge efforts have been invested in the last decade concerning the establishment of dedicated analysis methods and techniques for model transformations. The analysis of general properties such as termination and confluence as well as specific properties defined for one particular transformation have been studied for different transformation kinds and languages. What most transformation analyses have in common is that they consider the transformation specifications as their primary source. However, as I will show in my presentation, methods and techniques deployed for analysing potential transformation executions at runtime are needed as well. As transformation executions quickly span huge transformation spaces, I will show how to effectively analyse and guide transformation executions towards fulfilling multiple, potentially conflicting transformation goals by employing search-based techniques.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Neural Architecture Search as Program Transformation Exploration

Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations in order to exploit hardware parallelism and memory hierarchy. However, legality concerns mean they fail to exploit the natural robustness of neural networks. In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs. In this work, we express such neural architecture operations as program transformations whose legality depends on a notion of representational capacity. This allows them to be combined with existing transformations into a unified optimization framework. This unification allows us to express existing NAS operations as combinations of simpler transformations. Crucially, it allows us to generate and explore new tensor convolutions. We prototyped the combined framework in TVM and were able to find optimizations across different DNNs, that significantly reduce inference time - over 3$\times$ in the majority of cases. Furthermore, our scheme dramatically reduces NAS search time. Code is available at~\href{https://github.com/jack-willturner/nas-as-program-transformation-exploration}{this https url}

Search-based amorphous slicing

Amorphous slicing is an automated source code extraction technique with applications in many areas of software engineering, including comprehension, reuse, testing and reverse engineering. Algorithms for syntax-preserving slicing are well established, but amorphous slicing is harder because it requires arbitrary transformation; finding good general purpose amorphous slicing algorithms therefore remains as hard as general program transformation. In this paper we show how amorphous slices can be computed using search techniques. The paper presents results from a set of experiments designed to explore the application of genetic algorithms, hill climbing, random search and systematic search to a set of six subject programs. As a benchmark, the results are compared to those from an existing analytical algorithm for amorphous slicing, which was written specifically to perform well with the sorts of program under consideration. The results, while tentative at this stage, do give grounds for optimism. The search techniques proved able to reduce the size of the programs under consideration in all cases, sometimes equaling the performance of the specifically-tailored analytic algorithm. In one case, the search techniques performed better, highlighting a fault in the existing algorith

Search-Based Model Transformations with MOMoT

Many scenarios require flexible model transformations as their execution should of course produce models with the best possible quality. At the same time, transformation problems often span a very large search space with respect to possible transformation results. Thus, guidance for transformation executions to find good solutions without enumerating the complete search space is a must. This paper presents MOMoT, a tool combining the power of model transformation engines and meta-heuristics search algorithms. This allows to develop model transformation rules as known from existing approaches, but for guiding their execution, the transformation engineers only have to specify transformation goals, and then the search algorithms take care of orchestrating the set of transformation rules to find models best fulfilling the stated, potentially conflicting transformation goals. For this, MOMoT allows to use a variety of different search algorithms. MOMoT is available as an open-source Eclipse plug-in providing a non-intrusive integration of the Henshin graph transformation framework and the MOEA search algorithm framework

A local and global tour on MOMoT

Many model transformation scenarios require flexible execution strategies as they should produce models with the highest possible quality. At the same time, transformation problems often span a very large search space with respect to possible transformation results. Recently, different proposals for finding good transformation results without enumerating the complete search space have been proposed by using meta-heuristic search algorithms. However, determining the impact of the different kinds of search algorithms, such as local search or global search, on the transformation results is still an open research topic. In this paper, we present an extension to MOMoT, which is a search-based model transformation tool, for supporting not only global searchers for model transformation orchestrations, but also local ones. This leads to a model transformation framework that allows as the first of its kind multi-objective local and global search. By this, the advantages and disadvantages of global and local search for model transformation orchestration can be evaluated. This is done in a case-study-based evaluation, which compares different performance aspects of the local- and global-search algorithms available in MOMoT. Several interesting conclusions have been drawn from the evaluation: (1) local-search algorithms perform reasonable well with respect to both the search exploration and the execution time for small input models, (2) for bigger input models, their execution time can be similar to those of global-search algorithms, but global-search algorithms tend to outperform local-search algorithms in terms of search exploration, (3) evolutionary algorithms show limitations in situations where single changes of the solution can have a significant impact on the solution’s fitness.Ministerio de Economia y Competitividad TIN2015-70560-RJunta de Andalucía P12-TIC-186

The Nesterov-Todd Direction and its Relation to Weighted Analytic Centers

The subject of this report concerns differential-geometric properties of the Nesterov-Todd search direction for linear optimization over symmetric cones. In particular, we investigate the rescaled asymptotics of the associated flow near the central path. Our results imply that the Nesterov-Todd direction arises as the solution of a Newton system defined in terms of a certain transformation of the primal-dual feasible domain. This transformation has especially appealing properties which generalize the notion of weighted analytic centers for linear programming

Air investigation of some alternative methods to agrobacterium and protoplast transformation for introducing exogenous genes into plants, based on the use of pollen as a vector

Some of the problems involved in the search for alternative plant transformation vectors have been examined. In particular the use of pregerminated pollen as a direct transformation vector has been investigated. It has been shown that the protocols of Hess et al (1985) and De Vet et al (1985) are not easy to repeat with the levels of success claimed by the authors, and that these methods are unlikely to find general applicability to plant transformation in unmodified form. It has not been shown conclusively that these methods do not work, although the true levels of transformation may be considerably lower than those claimed. Some of the basic information required for application of the methods of de la Pefia et al (1987) to Petunia has been elucidated

TOR: modular search with hookable disjunction

Horn Clause Programs have a natural exhaustive depth-first procedural semantics. However, for many programs this semantics is ineffective. In order to compute useful solutions, one needs the ability to modify the search method that explores the alternative execution branches. Tor, a well-defined hook into Prolog disjunction, provides this ability. It is light-weight thanks to its library approach and efficient because it is based on program transformation. Tor is general enough to mimic search-modifying predicates like ECLiPSe's search/6. Moreover, Tor supports modular composition of search methods and other hooks. The Tor library is already provided and used as an add-on to SWI-Prolog.publisher: Elsevier articletitle: Tor: Modular search with hookable disjunction journaltitle: Science of Computer Programming articlelink: http://dx.doi.org/10.1016/j.scico.2013.05.008 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

General framework for quantum search algorithms

Grover's quantum search algorithm drives a quantum computer from a prepared initial state to a desired final state by using selective transformations of these states. Here, we analyze a framework when one of the selective trasformations is replaced by a more general unitary transformation. Our framework encapsulates several previous generalizations of the Grover's algorithm. We show that the general quantum search algorithm can be improved by controlling the transformations through an ancilla qubit. As a special case of this improvement, we get a faster quantum algorithm for the two-dimensional spatial search.Comment: revised versio