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Several research infrastructure clusters have highlightedthe need for long-term data preservation as part of the Research Data Management (RDM) lifecycle. TheEOSC Strategic Research and Innovation Agenda(SRIA) also emphasises the importance of advanced long-term preservation to allow reproducibility of research results towards a sustainable EOSC. The SRIA stimulates and encourages the development of innovative services supporting FAIR principles, as well as data stewardship and preservation across different phases of the research lifecycle using dedicated incentive schemes funded by the EC. Such schemes can include Pre-Commercial Procurement (PCP)/ Public Procurement of Innovation Solutions (PCP/PPI), consisting of financial instruments for co-funding and de-risking the creation of innovative services to be procured by public organisations, co-developed with the private sector. Started in January 2019, using the EC PCP instrument,ARCHIVERis a unique initiative currently running in the EOSC framework that is competitively procuring R&D services for archiving and digital preservation that keep the intellectual control of data and supports the requirements of European research infrastructures, following best practices. This lightning talk will explain how ARCHIVER is providing a substantial contribution to the long-term data preservation vision for the EOSC, proposing a set of innovative services together with a model for facilitating the procurement of commercially supported services beyond the lifetime of the project. ARCHIVER is also extending the concept of FAIR principles to other research associated products, like software, workflows, services and even infrastructures, taking into account the live data and tools that need to be preserved, treating FAIR in the long-term and not only at the outset. An example of this vision is the integration of FAIRsFAIR evaluator (F-UJI) for validation of the resulting ARCHIVER services being developed.

Over the last decades, several data preservation efforts have been undertaken by the HEP community, as experiments are not repeatable and consequently their data considered unique. ARCHIVER is a European Commission (EC) co-funded Horizon 2020 pre-commercial procurement project procuring R&D combining multiple ICT technologies including data-intensive scalability, network, service interoperability and business models, in a hybrid cloud environment. The results will provide the European Open Science Cloud (EOSC) with archival and preservation services covering the full research lifecycle. The services are co-designed in partnership with four research organisations (CERN, DESY, EMBL-EBI and PIC/IFAE) deploying use cases from Astrophysics, HEP, Life Sciences and Photon-Neutron Sciences creating an innovation ecosystem for specialist data archiving and preservation companies willing to introduce new services capable of supporting the expanding needs of research. The HEP use cases being deployed include the CERN Opendata portal, preserving a second copy of the completed BaBar experiment and the CERN Digital Memory digitising CERN’s multimedia archive of the 20th century. In parallel, ARCHIVER has established an Early Adopter programme whereby additional use cases can be incorporated at each of the project phases thereby expanding services to multiple research domains and countries.

With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall efficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services. With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using Tensorflow data parallel strategy. More specifically, we parallelize the training process on multiple GPUs and Google Tensor Processing Units (TPU) and we compare two algorithms: the TensorFlow built-in logic and a custom loop, optimised to have higher control of the elements assigned to each GPU worker or TPU core. The quality of the generated data is compared to Monte Carlo simulation. Linear speed-up of the training process is obtained, while retaining most of the performance in terms of physics results. Additionally, we benchmark the aforementioned approaches, at scale, over multiple GPU nodes, deploying the training process on different public cloud providers, seeking for overall efficiency and cost-effectiveness. The combination of data science, cloud deployment options and associated economics allows to burst out heterogeneously, exploring the full potential of cloud-based services.