Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity. The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.

Cloud-LSVA will create Big Data Technologies to address the open problem of a lack of software tools, and hardware platforms, to annotate petabyte scale video datasets. The problem is of particular importance to the automotive industry. CMOS Image Sensors for Vehicles are the primary area of innovation for camera manufactures at present. They are the sensor that offers the most functionality for the price in a cost sensitive industry. By 2020 the typical mid-range car will have 10 cameras, be connected, and generate 10TB per day, without considering other sensors. Customer demand is for Advanced Driver Assistance Systems (ADAS) which are a step on the path to Autonomous Vehicles. The European automotive industry is the world leader and dominant in the market for ADAS. The technologies depend upon the analysis of video and other vehicle sensor data. Annotations of road traffic objects, events and scenes are critical for training and testing computer vision techniques that are the heart of modern ADAS and Navigation systems. Thus, building ADAS algorithms using machine learning techniques require annotated data sets. Human annotation is an expensive and error-prone task that has only been tackled on small scale to date. Currently no commercial tool exists that addresses the need for semi-automated annotation or that leverages the elasticity of Cloud computing in order to reduce the cost of the task. Providing this capability will establish a sustainable basis to drive forward automotive Big Data Technologies. Furthermore, the computer is set to become the central hub of a connected car and this provides the opportunity to investigate how these Big Data Technologies can be scaled to perform lightweight analysis on board, with results sent back to a Cloud Crowdsourcing platform, further reducing the complexity of the challenge faced by the Industry. Car manufacturers can then in turn cyclically update the ADAS and Mapping software on the vehicle benefiting the consumer.