Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part II: application to partial differential equations

A template-based generic programming approach was presented in a previous paper that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations (PDEs). We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertainty quantification results for a 3D PDE application

CentiJ: An RMI Code Generator

The CentiJ system synthesizes Java source code that funnels invocations through an RMI (Remote Method Invocation) based transport layer for distributed computation. The technique generates bridge pattern code (i.e., interfaces and proxies) that automate the creation of virtual proxies for message forwarding. We examine the tradeoffs between bridge implementations based on manual static delegation, automatic static delegation, and dynamic proxy classes. Advantages of the CentiJ technique include improved performance, type safety, transparency, predictability, flexibility and reliability. We then look at various methods for solving the disambiguation problem that arises when delegates have conflicting method signatures. Disambiguation can be automatic, semi-automatic or manual. CentiJ can automatically create a class that alters the interface to the bridge (using the adapter pattern)

3rd international software language engineering conference (SLE) : pre-proceedings, October 12-13, 2010, Eindhoven, the Netherlands

We are pleased to present the proceedings of the Third International Conference on Software Language Engineering (SLE 2010). The conference will be held in Eindhoven, the Netherlands during October 12-13, 2010 and will be co-located with The Ninth International Conference on Generative Programming and Component Engineering (GPCE'10), and The Workshop on Feature-Oriented Software Development (FOSD). An important goal of SLE is to integrate the different sub-communities of the software-language-engineering community to foster cross-fertilization and strengthen research overall. The Doctoral Symposium at SLE 2010 contributes towards these goals by providing a forum for both early and late-stage PhD students to present their research and get detailed feedback and advice from other researchers. The SLE conference series is devoted to a wide range of topics related to artificial languages in software engineering. SLE is an international research forum that brings together researchers and practitioners from both industry and academia to expand the frontiers of software language engineering. SLE's foremost mission is to encourage and organize communication between communities that have traditionally looked at software languages from different, more specialized, and yet complementary perspectives. SLE emphasizes the fundamental notion of languages as opposed to any realization in specific technical spaces. In this context, the term "software language" comprises all sorts of artificial languages used in software development including general-purpose programming languages, domain-specific languages, modeling and meta-modeling languages, data models, and ontologies. Software language engineering is the application of a systematic, disciplined, quantifiable approach to the development, use, and maintenance of these languages. The SLE conference is concerned with all phases of the lifecycle of software languages; these include the design, implementation, documentation, testing, deployment, evolution, recovery, and retirement of languages. Of special interest are tools, techniques, methods, and formalisms that support these activities. In particular, tools are often based on, or automatically generated from, a formal description of the language. Hence, the treatment of language descriptions as software artifacts, akin to programs, is of particular interest - while noting the special status of language descriptions, and the tailored engineering principles and methods for modularization, refactoring, refinement, composition, versioning, co-evolution, and analysis that can be applied to them. The response to the call for papers for SLE 2010 was very enthusiastic. We received 79 full submissions from 108 initial abstract submissions. From these submissions, the Program Committee (PC) selected 25 papers: 17 full papers, five short papers, and two tool demonstration papers, resulting in an acceptance rate of 32%. To ensure the quality of the accepted papers, each submitted paper was reviewed by at least three PC members. Each paper was discussed in detail during the electronic PC meeting. A summary of this discussion was prepared by members of the PC and provided to the authors along with the reviews

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow

Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++ language to transform a given calculation into one that can compute a variety of additional quantities that are necessary for many state-of-the-art simulation and analysis algorithms. An approach for incorporating these ideas into complex simulation codes through general graph-based assembly is also presented. These ideas have been implemented within a set of packages in the Trilinos framework and are demonstrated on a simple problem from chemical engineering

Architecural Principles and Components of Adaptive Process Management Technology

Process-aware information systems (PAIS) must not freeze business processes, but should enable authorized users to deviate from the implemented workflows on-the-fly and to dynamically evolve them over time. While there has been a lot of work on the theoretical foundations of dynamic process changes, there is still a lack of implemented PAIS providing this dynamics. Designing the architecture of such adaptive PAIS, however, constitutes a big challenge due to the high complexity coming with dynamic changes. Besides this, performance, robustness, security and usability of the PAIS must not be affected by the added flexibility. In the AristaFlow project we follow a holistic approach to master this complexity. Based on a conceptual framework for adaptive process management, we have designed a sophisticated architecture for next generation process management technology. This paper discusses major design goals and basic architectural principles, gives insights into selected system components, and shows how change support features can be realized in an integrated and efficient manner

Architectural Design of Flexible Process Management Technology

To provide effective support, process-aware information systems (PAIS) must not freeze existing business processes. Instead they should enable authorized users to deviate on-the-fly from the implemented processes and to dynamically evolve them over time. While there has been a lot of work on the theoretical foundations of dynamic process changes, there is still a lack of PAIS implementing this dynamics. Designing the architecture of respective technology constitutes a big challenge due to the high complexity coming with dynamic processes. Besides this, performance, robustness, security and usability of the system must not be affected by the added flexibility. In the AristaFlow project we have taken a holistic approach to master this complexity. Based on a conceptual framework for flexible process enactment and dynamic processes, we have designed a sophisticated architecture for next generation process management technology. This paper discusses major design goals and basic architectural principles, gives insights into selected system components, and shows how change support features can be realized in an integrated and effective manner

Automatic differentiation in machine learning: a survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names "dynamic computational graphs" and "differentiable programming". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms "autodiff", "automatic differentiation", and "symbolic differentiation" as these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure

Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation

Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool `RobCoGen' to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning