Synchronous Modeling of Data Intensive Applications

In this report, we present the first results of a study on the modeling of data-intensive parallel applications following the synchronous approach. More precisely, we consider the Gaspard extension of Array-OL, which is dedicated to System-on-Chip codesign. We define an associated synchronous dataflow equational model that enables to address several design correctness issues (e.g. verification of frequency / latency constraints) using the formal tools and techniques provided by the synchronous technology. We particularly illustrate a synchronizability analysis using affine clock systems. Directions are drawn from these bases towards modeling hierarchical applications, and adding control automata involving verification

Coinductive Formal Reasoning in Exact Real Arithmetic

In this article we present a method for formally proving the correctness of the lazy algorithms for computing homographic and quadratic transformations -- of which field operations are special cases-- on a representation of real numbers by coinductive streams. The algorithms work on coinductive stream of M\"{o}bius maps and form the basis of the Edalat--Potts exact real arithmetic. We use the machinery of the Coq proof assistant for the coinductive types to present the formalisation. The formalised algorithms are only partially productive, i.e., they do not output provably infinite streams for all possible inputs. We show how to deal with this partiality in the presence of syntactic restrictions posed by the constructive type theory of Coq. Furthermore we show that the type theoretic techniques that we develop are compatible with the semantics of the algorithms as continuous maps on real numbers. The resulting Coq formalisation is available for public download.Comment: 40 page

Deconstructing Reo

AbstractCoordination in Reo emerges from the composition of the behavioural constraints of the primitives, such as channels, in a component connector. Understanding and implementing Reo, however, has been challenging due to interaction of the channel metaphor, which is an inherently local notion, and the non-local nature of constraint propagation imposed by composition. In this paper, the channel metaphor takes a back seat, and we focus on the behavioural constraints imposed by the composition of primitives, and phrase the semantics of Reo as a constraint satisfaction problem. Not only does this provide a clear intensional description of the behaviour of Reo connectors in terms of synchronisation and data flow constraints, it also paves the way for new implementation techniques based on constraint propagation and satisfaction. In fact, decomposing Reo into constraints provides a new computational model for connectors, which we extend to model interaction with an unknown external world beyond what is currently possible in Reo

Higher-Order, Data-Parallel Structured Deduction

State-of-the-art Datalog engines include expressive features such as ADTs (structured heap values), stratified aggregation and negation, various primitive operations, and the opportunity for further extension using FFIs. Current parallelization approaches for state-of-art Datalogs target shared-memory locking data-structures using conventional multi-threading, or use the map-reduce model for distributed computing. Furthermore, current state-of-art approaches cannot scale to formal systems which pervasively manipulate structured data due to their lack of indexing for structured data stored in the heap. In this paper, we describe a new approach to data-parallel structured deduction that involves a key semantic extension of Datalog to permit first-class facts and higher-order relations via defunctionalization, an implementation approach that enables parallelism uniformly both across sets of disjoint facts and over individual facts with nested structure. We detail a core language, $DL_s$, whose key invariant (subfact closure) ensures that each subfact is materialized as a top-class fact. We extend $DL_s$ to Slog, a fully-featured language whose forms facilitate leveraging subfact closure to rapidly implement expressive, high-performance formal systems. We demonstrate Slog by building a family of control-flow analyses from abstract machines, systematically, along with several implementations of classical type systems (such as STLC and LF). We performed experiments on EC2, Azure, and ALCF's Theta at up to 1000 threads, showing orders-of-magnitude scalability improvements versus competing state-of-art systems

Techniques for improving efficiency and scalability for the integration of information retrieval and databases

PhDThis thesis is on the topic of integration of Information Retrieval (IR) and Databases (DB), with particular focuses on improving efficiency and scalability of integrated IR and DB technology (IR+DB). The main purpose of this study is to develop efficient and scalable techniques for supporting integrated IR and DB technology, which is a popular approach today for handling complex queries over text and structured data. Our specific interest in this thesis is how to efficiently handle queries over large-scale text and structured data. The work is based on a technology that integrates probability theory and relational algebra, where retrievals for text and data are to be expressed in probabilistic logical programs such as probabilistic relational algebra or probabilistic Datalog. To support efficient processing of probabilistic logical programs, we proposed three optimization techniques that focus on aspects covered logical and physical layers, which include: scoring-driven query optimization using scoring expression, query processing with top-k incorporated pipeline, and indexing with relational inverted index. Specifically, scoring expressions are proposed for expressing the scoring or probabilistic semantics of implied scoring functions of PRA expressions, so that efficient query execution plan can be generated by rule-based scoring-driven optimizer. Secondly, to balance efficiency and effectiveness so that to improve query response time, we studied methods for incorporating topk algorithms into pipelined query execution engine for IR+DB systems. Thirdly, the proposed relational inverted index integrates IR-style inverted index and DB-style tuple-based index, which can be used to support efficient probability estimation and aggregation as well as conventional relational operations. Experiments were carried out to investigate the performances of proposed techniques. Experimental results showed that the efficiency and scalability of an IR+DB prototype have been improved, while the system can handle queries efficiently on considerable large data sets for a number of IR tasks

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks