GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP

Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.Comment: 34 pages, 26 figures, 24 table

BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems

In this report, we present work towards a framework for modeling and checking behavior of spatially distributed component systems. Design goals of our framework are the ability to model spatial behavior in a component oriented, simple and intuitive way, the possibility to automatically analyse and verify systems and integration possibilities with other modeling and verification tools. We present examples and the verification steps necessary to prove properties such as range coverage or the absence of collisions between components and technical details

Proceedings of the First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014): Porto, Portugal

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014

Smart Environments for Collaborative Design, Implementation, and Interpretation of Scientific Experiments

Ambient intelligence promises to enable humans to smoothly interact with their environment, mediated by computer technology. In the literature on ambient intelligence, empirical scientists are not often mentioned. Yet they form an interesting target group for this technology. In this position paper, we describe a project aimed at realising an ambient intelligence environment for face-to-face meetings of researchers with different academic backgrounds involved in molecular biology “omics” experiments. In particular, microarray experiments are a focus of attention because these experiments require multidisciplinary collaboration for their design, analysis, and interpretation. Such an environment is characterised by a high degree of complexity that has to be mitigated by ambient intelligence technology. By experimenting in a real-life setting, we will learn more about life scientists as a user group

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Business Process Risk Management and Simulation Modelling for Digital Audio-Visual Media Preservation.

Digitised and born-digital Audio-Visual (AV) content presents new challenges for preservation and Quality Assurance (QA) to ensure that cultural heritage is accessible for the long term. Digital archives have developed strategies for avoiding, mitigating and recovering from digital AV loss using IT-based systems, involving QA tools before ingesting files into the archive and utilising file-based replication to repair files that may be damaged while in the archive. However, while existing strategies are effective for addressing issues related to media degradation, issues such as format obsolescence and failures in processes and people pose significant risk to the long-term value of digital AV content. We present a Business Process Risk management framework (BPRisk) designed to support preservation experts in managing risks to long-term digital media preservation. This framework combines workflow and risk specification within a single risk management process designed to support continual improvement of workflows. A semantic model has been developed that allows the framework to incorporate expert knowledge from both preservation and security experts in order to intelligently aid workflow designers in creating and optimising workflows. The framework also provides workflow simulation functionality, allowing users to a) understand the key vulnerabilities in the workflows, b) target investments to address those vulnerabilities, and c) minimise the economic consequences of risks. The application of the BPRisk framework is demonstrated on a use case with the Austrian Broadcasting Corporation (ORF), discussing simulation results and an evaluation against the outcomes of executing the planned workflow

Development and Testing of a Software Framework for Controlling Humanoid Robots in Disaster-Response Scenarios

The aim of this thesis is to design and develop a modular software framework for controlling humanoid robots in teleoperation, in a context of disaster-response or civil defense. Over the years, natural (earthquakes, floods, etc.) or man-made disasters (nuclear reactor meltdowns, terrorist attacks, etc.) have caused several victims. The state of the art of disaster-robotics allows to deploy efficient and powerful robots in order to assist and support humans in the delicate phases of searching and rescuing survivors. In particular, with the use of teleoperation, the inclusion of a human operator (human-in-the-loop) can dramatically promote the application of humanoid robots, due to the human superior competence in critical thinking and context-awareness. This way, robots can be used as an interface between man and environment. Under these concepts, the thesis work focused on the design of a robust and efficient control architecture that brings whole-body locomotion and manipulation capabilities to the robot. Specifically, this thesis dealt with the development of a software module for teleoperating a robot while it is in a vehicle, making it able to drive. The module internal architecture is structured as a Finite State Machine, which allows to model a workflow of behaviors in an event-driven manner, providing safe and robust control in a teleoperation scenario. The effectiveness of the developed software has been validated during the DARPA Robotics Challenge Finals, occured in Pomona, CA (USA), on June 5-6 of 2015