Non-Strict Independence-Based Program Parallelization Using Sharing and Freeness Information.

The current ubiquity of multi-core processors has brought renewed interest in program parallelization. Logic programs allow studying the parallelization of programs with complex, dynamic data structures with (declarative) pointers in a comparatively simple semantic setting. In this context, automatic parallelizers which exploit and-parallelism rely on notions of independence in order to ensure certain efficiency properties. “Non-strict” independence is a more relaxed notion than the traditional notion of “strict” independence which still ensures the relevant efficiency properties and can allow considerable more parallelism. Non-strict independence cannot be determined solely at run-time (“a priori”) and thus global analysis is a requirement. However, extracting non-strict independence information from available analyses and domains is non-trivial. This paper provides on one hand an extended presentation of our classic techniques for compile-time detection of non-strict independence based on extracting information from (abstract interpretation-based) analyses using the now well understood and popular Sharing + Freeness domain. This includes algorithms for combined compile-time/run-time detection which involve special run-time checks for this type of parallelism. In addition, we propose herein novel annotation (parallelization) algorithms, URLP and CRLP, which are specially suited to non-strict independence. We also propose new ways of using the Sharing + Freeness information to optimize how the run-time environments of goals are kept apart during parallel execution. Finally, we also describe the implementation of these techniques in our parallelizing compiler and recall some early performance results. We provide as well an extended description of our pictorial representation of sharing and freeness information

Transformations of Logic Programs with Goals as Arguments

We consider a simple extension of logic programming where variables may range over goals and goals may be arguments of predicates. In this language we can write logic programs which use goals as data. We give practical evidence that, by exploiting this capability when transforming programs, we can improve program efficiency. We propose a set of program transformation rules which extend the familiar unfolding and folding rules and allow us to manipulate clauses with goals which occur as arguments of predicates. In order to prove the correctness of these transformation rules, we formally define the operational semantics of our extended logic programming language. This semantics is a simple variant of LD-resolution. When suitable conditions are satisfied this semantics agrees with LD-resolution and, thus, the programs written in our extended language can be run by ordinary Prolog systems. Our transformation rules are shown to preserve the operational semantics and termination.Comment: 51 pages. Full version of a paper that will appear in Theory and Practice of Logic Programming, Cambridge University Press, U

Transforming Normal Programs by Replacement

The replacement transformation operation, already defined in [28], is studied wrt normal programs. We give applicability conditions able to ensure the correctness of the operation wrt Fitting's and Kunen's semantics. We show how replacement can mimic other transformation operations such as thinning, fattening and folding, thus producing applicability conditions for them too. Furthermore we characterize a transformation sequence for which the preservation of Fitting's and Kunen's semantics is ensured

Bayesian Logic Programs

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developedComment: 52 page

A finite-valued solver for disjunctive fuzzy answer set programs

Fuzzy Answer Set Programming (FASP) is a declarative programming paradigm which extends the flexibility and expressiveness of classical Answer Set Programming (ASP), with the aim of modeling continuous application domains. In contrast to the availability of efficient ASP solvers, there have been few attempts at implementing FASP solvers. In this paper, we propose an implementation of FASP based on a reduction to classical ASP. We also develop a prototype implementation of this method. To the best of our knowledge, this is the first solver for disjunctive FASP programs. Moreover, we experimentally show that our solver performs well in comparison to an existing solver (under reasonable assumptions) for the more restrictive class of normal FASP programs

Funding the Extraordinary: An Evaluation of The Kresge Foundation Arts and Culture Program's Institutional Capitalization Grantmaking

In undertaking an assessment of Kresge's Capitalization Program, NFF applied its own high-level framework to help answer Kresge's primary research questions and assess the progress of each grantee in meeting its stated capital targets. NFF has found that effective capitalization in the nonprofit sector requires attention to three key financial priorities: liquidity, adaptability and durability:1. Liquidity: Does the organization have adequate cash to meet its operating needs?2. Adaptability: Does the organization have flexible funds that allow it to make adjustments as circumstances change?3. Durability: Does the organization have sufficient resources to address the range of needs that it may face in future years?NFF's review of Kresge's grantees sought to assess capitalization by looking for evidence of organizational progress in building liquid funds for immediate operating needs, as well as longer-term balance sheet savings for adaptability and durability. NFF's evaluation of grantees involved a combination of data analysis and interviews

Online Tensor Methods for Learning Latent Variable Models

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.Comment: JMLR 201