Exceptions for Algorithmic Skeletons

to appearInternational audienceAlgorithmic Skeletons offer high-level abstractions for parallel programming based on recurrent parallelism patterns. Patterns can be combined and nested into more complex parallelism behaviors. Programmers fill the skeleton patterns with the functional (business) code, which transforms the generic skeleton into a specific application. However, when the functional code generate exceptions, this exposes the programmer to details of the skeleton library, breaking the high-level abstraction principle. Furthermore, related parallel activities must be stopped as the exception is raised. This paper describes how to handle exceptions in Algorithmic Skeletons without breaking the high-level abstractions of the programming model. We describe both the behavior of the framework in a formal way, and its implementation in Java: the Skandium Library

Defining and Checking Deployment Contracts for Software Components

Abstract. Ideally in the deployment phase, components should be composable, and their composition checked. Current component models fall short of this ideal. Most models do not allow composition in the deployment phase. Moreover, current models use only deployment descriptors as deployment contracts. These descriptors are not ideal contracts. For one thing, they are only for specific containers, rather than arbitrary execution environments. In any case, they are checked only at runtime, not deployment time. In this paper we present an approach to component deployment which not only defines better deployment contracts but also checks them in the deployment phase.

The OMERACT Stepwise Approach to Select and Develop Imaging Outcome Measurement Instruments: The Musculoskeletal Ultrasound Example

Objective. To describe the Outcome Measures in Rheumatology (OMERACT) stepwise approach to select and develop an imaging instrument with musculoskeletal ultrasound (US) as an example. Methods. The OMERACT US Working Group (WG) developed a 4-step process to select instruments based on imaging. Step 1 applies the OMERACT Framework Instrument Selection Algorithm (OFISA) to existing US outcome measurement instruments for a specific indication. This step requires a literature review focused on the truth, discrimination, and feasibility aspects of the instrument for the target pathology. When the evidence is completely unsatisfactory, Step 2 is a consensus process to define the US characteristics of the target pathology including one or more so-called “elementary lesions”. Step 3 applies the agreed definitions to the image, evaluates their reliability, develops a severity grading of the lesion(s) at a given anatomical site, and evaluates the effect of the acquisition technique on feasibility and lesion(s) detection. Step 4 applies and assesses the definition(s) and scoring system(s) in cross-sectional studies and multicenter trials. The imaging instrument is now ready to pass a final OFISA check. Results. With this process in place, the US WG now has 18 subgroups developing US instruments in 10 different diseases. Half of them have passed Step 3, and the groups for enthesitis (spondyloarthritis, psoriatic arthritis), synovitis, and tenosynovitis (rheumatoid arthritis) have finished Step 4. Conclusion. The US WG approach to select and develop outcome measurement instruments based on imaging has been repeatedly and successfully applied in US, but is generic for imaging and fits with OMERACT Filter 2.1