API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library

ShapeClip: towards rapid prototyping with shape-changing displays for designers

This paper presents ShapeClip: a modular tool capable of transforming any computer screen into a z-actuating shape-changing display. This enables designers to produce dynamic physical forms by "clipping" actuators onto screens. ShapeClip displays are portable, scalable, fault-tolerant, and support runtime re-arrangement. Users are not required to have knowledge of electronics or programming, and can develop motion designs with presentation software, image editors, or web-technologies. To evaluate ShapeClip we carried out a full-day workshop with expert designers. Participants were asked to generate shape-changing designs and then construct them using ShapeClip. ShapeClip enabled participants to rapidly and successfully transform their ideas into functional systems

Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)

The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts. We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs. More generally, we are interested in building a community that can drive forward the development of modern DSLs. These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition

Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here

Modularity for Large Virtual Reality Applications

International audienceThis paper focuses on the design of high performance VR applications. These applications usually involve various I/O devices and complex simulations. A parallel architecture or grid infrastructure is required to provide the necessary I/O and processing capabilities. Developing such applications faces several difficulties, two important ones being software engineering and performance issues. We argue that application modularity is a key concept to help the developer handle the complexity of these applications. We discuss how various approaches borrowed from other existing works can be combined to significantly improve the modularity of VR applications. This led to the development of the FlowVR middleware that associates a data-flow model with a hierarchical component model. Different case studies are presented to discuss the benefits of the approach proposed

Bluefish: A Relational Framework for Graphic Representations

Complex graphic representations -- such as annotated visualizations, molecular structure diagrams, or Euclidean geometry -- convey information through overlapping perceptual relations. To author such representations, users are forced to use rigid, purpose-built tools with limited flexibility and expressiveness. User interface (UI) frameworks provide only limited relief as their tree-based models are a poor fit for expressing overlaps. We present Bluefish, a diagramming framework that extends UI architectures to support overlapping perceptual relations. Bluefish graphics are instantiated as relational scenegraphs: hierarchical data structures augmented with adjacency relations. Authors specify these relations with scoped references to components found elsewhere in the scenegraph. For layout, Bluefish lazily materializes necessary coordinate transformations. We demonstrate that Bluefish enables authoring graphic representations across a diverse range of domains while preserving the compositional and abstractional affordances of traditional UI frameworks. Moreover, we show how relational scenegraphs capture previously latent semantics that can later be retargeted (e.g., for screen reader accessibility).Comment: 27 pages, 14 figure

Improving programmability and performance for scientific applications

With modern advancements in hardware and software technology scaling towards new limits, our compute machines are reaching new potentials to tackle more challenging problems. While the size and complexity of both the problems and solutions increases, the programming methodologies must remain at a level that can be understood by programmers and scientists alike. In our work, this problem is encountered when developing an optimized framework to best exploit the semantic properties of a finite-element solver. In efforts to address this problem, we explore programming and runtime models which decouple algorithmic complexity, parallelism concerns, and hardware mapping. We build upon these frameworks to exploit domain-specific semantics using high-level transformations and modifications to obtain performance through algorithmic and runtime optimizations. We first discusses optimizations performed on a computational mechanics solver using a novel coupling technique for multi-time scale methods for discrete finite element domains. We exploit domain semantics using a high-level dynamic runtime scheme to reorder and balance workloads to greatly improve runtime performance. The framework presented automatically chooses a near-optimal coupling solution and runs a work-stealing parallel executor to run effectively on multi-core systems. In my latter work, I focus on the parallel programming model, Concurrent Collections (CnC), to seamlessly bridge the gap between performance and programmability. Because challenging problems in various domains, not limited to computation mechanics, requires both domain expertise and programming prowess, there is a need for ways to separate those concerns. This thesis describes methods and techniques to obtain scalable performance using CnC programming while limiting the burden of programming. These high level techniques are presented for two high-performance applications corresponding to hydrodynamics and multi-grid solvers

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.Comment: 72 page