Why not empower knowledge workers and lifelong learners to develop their own environments?

In industrial and educational practice, learning environments are designed and implemented by experts from many different fields, reaching from traditional software development and product management to pedagogy and didactics. Workplace and lifelong learning, however, implicate that learners are more self-motivated, capable, and self-confident in achieving their goals and, consequently, tempt to consider that certain development tasks can be shifted to end-users in order to facilitate a more flexible, open, and responsive learning environment. With respect to streams like end-user development and opportunistic design, this paper elaborates a methodology for user-driven environment design for action-based activities. Based on a former research approach named 'Mash-Up Personal Learning Environments'(MUPPLE) we demonstrate how workplace and lifelong learners can be empowered to develop their own environment for collaborating in learner networks and which prerequisites and support facilities are necessary for this methodology

Artefact Ecologies: Supporting Embodied Meeting Practices with Distance Access

Frameworks such as activity theory, distributed cognition and structuration theory, amongst others, have shown that detailed study of contextual settings where users work (or live) can help the design of interactive systems. However, these frameworks do not adequately focus on accounting for the materiality (and embodiment) of the contextual settings. Within the IST-EU funded AMIDA project (Augmented Multiparty Interaction with Distance Access) we are looking into supporting meeting practices with distance access. Meetings are inherently embodied in everyday work life and that material artefacts associated with meeting practices play a critical role in their formation. Our eventual goal is to develop a deeper understanding of the dynamic and embodied nature of meeting practices and designing technologies to support these. In this paper we introduce the notion of "artefact ecologies" as a conceptual base for understanding embodied meeting practices with distance access. Artefact ecologies refer to a system consisting of different digital and physical artefacts, people, their work practices and values and lays emphasis on the role artefacts play in embodiment, work coordination and supporting remote awareness. In the end we layout our plans for designing technologies for supporting embodied meeting practices within the AMIDA project. \u

Preparing sparse solvers for exascale computing.

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'

What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM