Thinking BigData: Motivation, Results and a Few Recipes for a Balanced Growth of High Performance Computing in Academia

Big Data is today both an emerging research area and a real present and future demand. High Performance Computing (HPC) Centers cannot neglect this fact and have to be reshaped to fulfill this need. In this paper we share our experience of upgrading a HPC Center at Politecnico di Torino, originally designed and deployed in 2010. We believe that this issue could be common to some other existing "general purpose" HPC centers where, at least in the short term, the possibility to start from scratch a new Big Data HPC center cannot be afforded but a balanced upgrade of the existing system has to be preferre

Hadoop on a Low-Budget General Purpose HPC Cluster in Academia

In the last decade, we witnessed an increasing interest in High Performance Computing (HPC) infrastructures, which play an important role in both academic and industrial research projects. At the same time, due to the increasing amount of available data, we also witnessed the introduction of new frameworks and applications based on the MapReduce paradigm (e.g., Hadoop). Traditional HPC systems are usually designed for CPU- and memory-intensive applications. However, the use of already available HPC infrastructures for data-intensive applications is an interesting topic, in particular in academia where the budget is usually limited and the same cluster is used by many researchers with different requirements. In this paper, we investigate the integration of Hadoop, and its performance, in an already existing low-budget general purpose HPC cluster characterized by heterogeneous nodes and a low amount of secondary memory per node

Hadoop on a Low-Budget General Purpose HPC Cluster in Academia

Abstract. In the last decade, we witnessed an increasing interest in High Performance Computing (HPC) infrastructures, which play an important role in both academic and industrial research projects. At the same time, due to the increasing amount of available data, we also witnessed the introduction of new frameworks and applications based on the MapReduce paradigm (e.g., Hadoop). Traditional HPC systems are usually designed for CPU-and memory-intensive applications. However, the use of already available HPC infrastructures for data-intensive applications is an interesting topic, in particular in academia where the budget is usually limited and the same cluster is used by many researchers with different requirements. In this paper, we investigate the integration of Hadoop, and its performance, in an already existing low-budget general purpose HPC cluster characterized by heterogeneous nodes and a low amount of secondary memory per node

HPC4AI, an AI-on-demand federated platform endeavour

In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turin's Centre on High- Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5Me and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small- Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood

The 'Digital Twin' to enable the vision of precision cardiology

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.L'Institut de Rythmologie et modélisation Cardiaqu