Bull SAS | Date: 2017-04-19
The invention relates to a device for searching for element correspondence in a list, said device comprising: a plurality of content-adressable memory modules configured so as to be able to compare in parallel an input element with the content thereof, the list being represented by the concatenation of the valid content of said memories in an order defined by a list of priority, a module for the determination, in said list of priority, of the first module wherein the input element corresponds to an element stored in said module, and a module for reading the first element of said determined module corresponding to the input element.
Bull S.A.S. | Date: 2017-04-12
The invention relates to a method for managing end-to-end reliability in data delivery with acknowledgement from a source node (10) to a group of destination nodes (21-23), said method including the following steps: marking the messages (1) transmitted from the source node (10); upon transmitting a message, incrementing a global sequence number; identifying the global sequence number of a transmitted message for which the source node has not received an acknowledgement; calculating the difference between the global sequence number of the next message to be transmitted and the global sequence number identified; if the calculated difference is equal to a predefined threshold, suspending the transmission of messages from the source node (10) to the group of destination nodes (21-23); and deducing the presence of an error in the data delivery.
Bull SAS | Date: 2017-03-29
A method makes it possible to obtain information stored in registers (R11 -R4J) of at least one processing module (MT1 -MTJ) of a computer (CA), each processing module (MT1 -MTJ) furthermore comprising a management controller (CG1 -CGJ) able to read the information stored in the associated registers (R11 -R4J) and a programmable logic circuit (CL1 -CLJ) to trigger a reset requested following a fatal error. Accordingly, in the event of reception of a reset request by a programmable logic circuit (CL2) of a processing module (MT2), this programmable logic circuit (CL2) suspends the triggering of this reset and alerts the occurrence of a fatal error to the associated management controller (CG2) which, if it is capable thereof, reads the information stored in associated and chosen registers (R12-R42), then stores this read information in a file, then the associated programmable logic circuit (CL2) is authorized to trigger the requested reset.
Bull Sas | Date: 2017-02-22
Metrology system (2) for the management of observation data and the quality of the air, this system being configured to collect at least one observation datum, and to associate with this observation datum a quality code reflecting the utilizable character of this observation datum with respect to a predefined quality criterion, this system comprising: - a data acquisition module (21); - a centralized data management module (22); - a data presentation and dissemination module (23); and - transverse functional bricks (24): o for data processing and production; o for end-to-end data quality control; o for intermediation so as to urbanize the architecture and allow the exposition of services.
Bull S.A.S. | Date: 2017-05-24
Method of guiding a terminal (4) within a self-service shop (2), this method comprising the following operations: acquiring a basket; calculating a path (18), comprising a succession of routes between the points (PVi) of sale of the items of the basket; locating in real time the terminal (4) within the shop (2); displaying the route in progress as long as the current position of the terminal (4) within the shop (2) coincides with the path (18): when a deviation of the terminal (4) from the path (18) is detected: calculating a new path (18); when the proximity of the terminal (4) to a point (PVi) of sale of the new path (18) is detected, displaying a route of the new path (18) having as origin the current position of the terminal (4) and as destination this point (PVi) of sale.
Bull SAS | Date: 2017-02-01
Method for assisting the movement of an agent in an interior environment. Method allowing the calculation of an itinerary on a dynamics graph. The dynamics graph is produced by those circulating in the zone to be mapped, the paths travelled being recorded by means of a geopositioning system compatible with inside use. The zone to be mapped is modelled in elementary volumes recoded in a zone database. The recorded paths are used to create a graph whereon an itinerary is calculated between two points, each of which being associated with an elementary volume of the zone.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-04-2015 | Award Amount: 3.20M | Year: 2016
Computer systems have faced significant power challenges over the past 20 years; these challenges have shifted from the devices and circuits level, to their current position as first-order constraints for system architects and software developers. TANGOs goal is to characterise factors which affect power consumption in software development and operation for heterogeneous parallel hardware environments. Our main contribution is the combination of requirements engineering and design modelling for self-adaptive software systems, with power consumption awareness in relation to these environments. The energy efficiency and application quality factors are integrated in the application lifecycle (design, implementation, operation). To support this, the key novelty of the project is a reference architecture and its implementation. Moreover, a programming model with built-in support for various hardware architectures including heterogeneous clusters, heterogeneous chips and programmable logic devices will be provided. TANGO will create a new cross-layer programming approach for heterogeneous parallel hardware architectures featuring automatic code generation including software and hardware modelling. This will consider power, performance, data location and time criticality optimization, in addition to security and dependability on the target hardware architecture. These results will be demonstrated in two real-world applications: reconfigurable power optimized connected platform and HPC. In order to improve collaboration and sustainability of TANGOs and fellow projects results, TANGO considers the foundation of a Research Alliance in which complementary research efforts into novel programming approaches will nucleate, leading to a strong research collaboration and effective integration of project results.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: EINFRA-5-2015 | Award Amount: 4.94M | Year: 2016
This Centre of Excellence will advance the role of computationally based modelling and simulation within biomedicine. Three related user communities lie at the heart of the CoE: academic, industrial and clinical researchers who all wish to build, develop and extend such capabilities in line with the increasing power of high performance computers. Three distinct exemplar research areas will be pursued: cardiovascular, molecularly-based and neuro-musculoskeletal medicine. Predictive computational biomedicine involves applications that are comprised of multiple components, arranged as far as possible into automated workflows in which data is taken, from an individual patient, processed, and combined into a model which produces predicted health outcomes. Many of the models are multiscale, requiring the coupling of two or more high performance codes. Computational biomedicine holds out the prospect of predicting the effect of personalised medical treatments and interventions ahead of carrying them out, with all the ensuing benefits. Indeed, in some cases, it is already doing so today. The CoE presents a powerful consortium of partners and has an outward facing nature and will actively train, disseminate and engage with these user communities across Europe and beyond. Because this field is new and growing rapidly, it offers numerous innovative opportunities. There are three SMEs and three enterprises within the project, as well as eight associate partners drawn from across the biomedical sector, who are fully aware of the vast potential of HPC in this domain. We shall work with them to advance the exploitation of HPC and will engage closely with medical professionals through our partner hospitals in order to establish modeling and simulation as an integral part of clinical decision making. Our CoE is thus user-driven, integrated, multidisciplinary, and distributed; presenting a vision that is in line with the Work Programme.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-04-2015 | Award Amount: 6.28M | Year: 2016
VINEYARD will develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by using typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). VINEYARD will develop two types of energy-efficient servers integrating two novel hardware accelerator types: coarse-grain programmable dataflow engines and fine-grain all-programmable FPGAs that accommodate multiple ARM cores. The former will be suitable for data centre applications that can be represented in dataflow graphs while the latter will be used for accelerating applications that need tight communication between the processor and the hardware accelerators. Both types of programmable accelerators will be customized based on application requirements, resulting in higher performance and significantly reduced energy budgets. VINEYARD will additionally develop a new programming framework and the required system software to hide the programming complexity of the resulting heterogeneous system based on the hardware accelerators. This programming framework will also allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer efficient energy use. VINEYARD will foster the expansion of the soft-IP cores industry, currently limited in the embedded systems, to in data centre market. The VINEYARD consortium has strong industrial foundations, and covers the whole value chain in the data-centre ecosystem; from the data-centre vendors up to the data-centre application programmers. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases a) a bioinformatics application for high-accuracy brain modelling, b) two critical financial applications and c) a big-data analysis application.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-06-2016 | Award Amount: 4.83M | Year: 2016
The project aims at producing a European Cloud Database Appliance for providing a Database as a Service able to match the predictable performance, robustness and trustworthiness of on premise architectures such as those based on mainframes. The project will evolve cloud architectures to enable the increase of the uptake of cloud technology by providing the robustness, trustworthiness, and performance required for applications currently considered too critical to be deployed on existing clouds. CloudDBAppliance will deliver a cloud database appliance featuring: 1. A scalable operational database able to process high update workloads such as the ones processed by banks or telcos, combined with a fast analytical engine able to answer analytical queries in an online manner. 2. A Hadoop data lake integrated with the operational database to cover the needs from companies on big data. 3. A cloud hardware appliance leveraging the next generation of hardware to be produced by Bull, the main European hardware provider. This hardware is a scale-up hardware similar to the one of mainframes but with a more modern architecture. Both the operational database and the in-memory analytics engine will be optimized to fully exploit this hardware and deliver predictable performance. Additionally, CloudDBAppliance will deal with the need to tolerate catastrophic cloud data centres failures (e.g. a fire or natural disaster) providing data redundancy across cloud data centres.