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Abstract. This work summarizes the possibilities offered by parallel programming environment ASSIST by outlining some of the features that will be demonstrated at the conference demo session. We'll substantially show how this environment can be deployed on a Linux workstation network/cluster, how applications can be compiled and run using ASSIST and eventually, we'll discuss some ASSIST scalability and performance features. We'll also outline how the ASSIST environment can be used to target GRID architectures.Keywords. Structured parallel programming, skeletons, coordination languages. Demo BackgroundASSIST (A Software development System based on Integrated Skeleton Technology) is a parallel programming environment based on skeleton and coordination language technology [8,9,3,2]. ASSIST provides the user/programmers with a structured parallel programming language (ASSISTcl), an integrated set of compiling tools (astCC) and a portable run time (the actual runtime CLAM, and the loader/runner assistrun). ASSIST is based on both skeleton and coordination languages technology, and comes after some other different experiences of our group related to skeleton based parallel programming [5,4]. It builds on the experience gained in these projects.The main goals in the design of ASSIST have been: high level programmability, rapid prototyping and suitability for complex multidisciplinary applications; functional and performance portability across a range of different target architectures; software reuse and interoperability. These goals have been achieved by taking a number of design choices and using several different implementation techniques.

This paper describes a data-driven Decision Support System for Electroencephalography (EEG) signals acquisition, and parallel elaboration based on the integration of an Ambient Intelligent (AmI) [1] platform and a GRID enabled Infrastructure. The paper explores the analysis and design of the environment, the real-time data acquisition, the integration of the acquired data in dedicated EHR, and the EEG processing through parallel analysis algorithm available on the GRID infrastructure. After an overview of background concepts, the paper presents a brief description of the environment architecture, and a detailed analysis of the EEG algorithm. The challenge of the work presented is to effectively show how medical data can be shared and processed by exploiting the resources and capabilities of both the AmI platform and the GRID infrastructure. This particular Decision Support System, shows how it is possible to improve patient safety, quality of care, and efficiency in healthcare delivery.