Marker-Directed Optimization of UnCAL Graph Transformations

Abstract

Abstract. Buneman et al. proposed a graph algebra called UnCAL (Unstructured CALculus) for compositional graph transformations based on structural recursion, and we have recently applied to model transformations. The compositional nature of the algebra greatly enhances the modularity of transformations. However, intermediate results generated between composed transformations cause overhead. Buneman et al. proposed fusion rules that eliminate the intermediate results, but auxiliary rewriting rules that enable the actual application of the fusion rules are not apparent so far. UnCAL graph model includes the concept of markers, which correspond to recursive function call in the structural recursion. We have found that there are many optimization opportunities at rewriting level based on static analysis, especially focusing on markers. The analysis can safely eliminate redundant function calls. Performance evaluation shows its practical effectiveness for non-trivial examples in model transformations.

Let High-level Graph Queries Be Parallel Efficient: An Approach Over Structural Recursion On Pregel

“…We therefore prepared two queries, named q 7 and q 8 in Spark SQL [2] using Left-Outer-Join, that query the same results as q 7 and q 8 respectively, in order to 1) validate the correctness of our parallel-efficient queries generation, 2) compare the performance of our solution to an industrial solution also under Spark implementation. Our solution is slower than Spark SQL for simple queries, e.g.…”

“…Our solution is slower than Spark SQL for simple queries, e.g. q 7 , but faster than Spark SQL for complex queries that contains many joins, e.g., q 8 . Looking at the Table 2, we see that, for q 7 , Spark SQL is much faster than our solution.…”

“…Structural recursion on data graphs was also studied for graph transformation [8]. Sequential evaluation of graph structural recursion has also been improved [7]. Yet, none of the previous studies practically focused on efficiently supporting largescale graphs on a distributed environment.…”

GRoundTram: An integrated framework for developing well-behaved bidirectional model transformations

“…GRoundTram Implementation on Bidirectional UnCAL Engine cept models that are described by edge-labeled graphs which are general enough to capture various kinds of models. We implement the GRoundTram system upon the powerful engine of bidirectional UnCAL, where a set of language-based tools have been developed: a bidirectional interpreter [14], a graph and graph transformation verifier [17], an optimizer to improve efficiency [18], and a checker of valid updates in the backward transformation [19]. The key contributions in this implementation are (1) a translation of UnQL + to UnCAL to enable use of the engine of bidirectional UnCAL, and (2) a bidirectional graph contraction algorithm for contracting bisimilar UnCAL graphs so that a usual model can have a bidirectional correspondence with an UnCAL graph.…”

GRoundTram: An integrated framework for developing well-behaved bidirectional model transformations

“…We provide a new model transformation language UnQL + , and we accept models that are described by edge-labeled graphs which are general enough to capture various kinds of models. We implement the GRoundTram system upon the powerful engine of bidirectional UnCAL, where a set of language-based tools have been developed: a bidirectional interpreter [14], a graph and graph transformation verifier [17], an optimizer to improve efficiency [18], and a checker of valid updates in the backward transformation [19]. The key contributions in this implementation are (1) a translation of UnQL + to UnCAL to enable use of the engine of bidirectional UnCAL, and (2) a bidirectional graph contraction algorithm for contracting …”