Group Communication Patterns for High Performance Computing in Scala

We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable/Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 % efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000

Liberating Composition from Language Dictatorship

Historically, programming languages have been—although benevolent—dictators: fixing a lot of semantics into built-in language constructs. Over the years, (some) programming languages have freed the programmers from restrictions to use only built-in libraries, built-in data types, or built-in type checking rules. Even though, arguably, such freedom could lead to anarchy, or people shooting themselves in the foot, the contrary tends to be the case: a language that does not allow for extensibility, is depriving software engineers from the ability to construct proper abstractions and to structure software in the most optimal way. Instead, the software becomes less structured and maintainable than would be possible if the software engineer could express the behavior of the program with the most appropriate abstractions. The new idea proposed by this paper is to move composition from built-in language constructs to programmable, first-class abstractions in the language. As an emerging result, we present the Co-op concept of a language, which shows that it is possible with a relatively simple model to express a wide range of compositions as first-class concepts

Reify Your Collection Queries for Modularity and Speed!

Modularity and efficiency are often contradicting requirements, such that programers have to trade one for the other. We analyze this dilemma in the context of programs operating on collections. Performance-critical code using collections need often to be hand-optimized, leading to non-modular, brittle, and redundant code. In principle, this dilemma could be avoided by automatic collection-specific optimizations, such as fusion of collection traversals, usage of indexing, or reordering of filters. Unfortunately, it is not obvious how to encode such optimizations in terms of ordinary collection APIs, because the program operating on the collections is not reified and hence cannot be analyzed. We propose SQuOpt, the Scala Query Optimizer--a deep embedding of the Scala collections API that allows such analyses and optimizations to be defined and executed within Scala, without relying on external tools or compiler extensions. SQuOpt provides the same "look and feel" (syntax and static typing guarantees) as the standard collections API. We evaluate SQuOpt by re-implementing several code analyses of the Findbugs tool using SQuOpt, show average speedups of 12x with a maximum of 12800x and hence demonstrate that SQuOpt can reconcile modularity and efficiency in real-world applications.Comment: 20 page

Splitting a pair: playing the gender card and the race card in American politics

More than any election before, the 2008 Presidential race revealed a persistent discussion of “race cards” and “gender cards.” In spite of the reported consensus that these alleged cards were everywhere, we know relatively little about those situations where the “card” label was applied, and even less about how this label influenced voters. In fact, among key electoral sources – political elites who use identity as a campaign tool, the journalists who cover and narrate elections, and researchers who make sense of elections-based behavior – there is no consensus regarding what a card is, how or when they are played, or who does the playing. This project seeks to begin to fill the gap in our knowledge of cards in campaigns by asking how were race and gender cards reported in news coverage of the 2008 presidential election, and how does labeling an appeal a “card” matter? Using content analysis and a two-part experiment, this study succeeds in drawing a much clearer picture of how card coverage, as an essential tool of narrating an election where women and racial minorities are present, affects American politics. While much of the research on cards defines their application and effects in terms of public policy issues, an examination of card coverage during the 2008 election reveals that much of the alleged cards were character-based. Moreover, the “card” label was not just used to categorize an appeal; cards were also invoked to maintain the identity narrative, even when identity was not a campaign issue. Using some of the most commonly reported cards from the 2008 race, the progressive experiments here revealed that, while the card label itself has little effect on how voters evaluate candidates, the addition of contextual information – for those with higher levels of racism and sexism – predicted increased support for white and male candidates, respectively. In short, these results show that how cards are covered defies our existing understanding of what a race card or a gender card is; moreover, in card coverage, the “card” label itself matters less than traditional cues like candidate sex and race in informing evaluations

Generating a Catalog of Unanticipated Schemas in Class Hierarchies using Formal Concept Analysis

International audienceContext: Inheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy. Objective: Although these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them. Method: To tackle this problem, we have developed an approach based on Formal Concept Analysis. Our FoCARE methodology (Formal Concept Analysis based-Reverse Engineering) identifies undocumented hi- erarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses. Results: We validate our approach by applying it to a large and non-trivial case study, yielding a catalog of Hierarchy Schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas

AspectMaps: Extending Moose to visualize AOP software

International audienceWhen using aspect-oriented programming the application implicitly invokes the functionality contained in the aspects. Consequently program comprehension of such a software is more intricate. To alleviate this difficulty we developed the AspectMaps visualization and tool. AspectMaps extends the Moose program comprehension and reverse engineering platform with support for aspects, and is implemented using facilities provided by Moose. In this paper we present the AspectMaps tool, and show how it can be used by performing an exploration of a fairly large aspect-oriented application. We then show how we extended the FAMIX meta-model family that underpins Moose to also provide support for aspects. This extension is called ASPIX, and thanks to this enhancement Moose can now also treat aspect-oriented software. Finally, we report on our experiences using some of the tools in Moose; Mondrian to implement the visualization, and Glamour to build the user interface. We discuss how we were able to implement a sizable visualization tool using them and how we were able to deal with some of their limitations. Note: This paper uses colors extensively. Please use a color version to better understand the ideas presented here