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none 12 si In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM’s POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform. open Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone

The increasing use of Internet-of-Things (IoT) devices for monitoring a wide spectrum of applications, along with the challenges of “big data” streaming support they often require for data analysis, is nowadays pushing for increased attention to the emerging edge computing paradigm. In particular, smart approaches to manage and analyze data directly on the network edge, are more and more investigated, and artificial intelligence (AI)-powered edge computing is envisaged to be a promising direction. In this article, we focus on data centers (DCs) and supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. In detail, we report on a novel lightweight and scalable approach to increase the security of DCs/SCs, which involves AI-powered edge computing on high-resolution power consumption. The method—called pAElla —targets real-time malware detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves power spectral density of power measurements, along with autoencoders. Results are promising, with an F1-score close to 1, and a false alarm and malware miss rate close to 0%. We compare our method with State-of-the-Art (SoA) MD techniques and show that, in the context of DCs/SCs, pAElla can cover a wider range of malware, significantly outperforming SoA approaches in terms of accuracy. Moreover, we propose a methodology for online training suitable for DCs/SCs in production, and release open data set and code.

ACM International Conference Proceeding Series, ICPP 2019: Proceedings of the 48th International Conference on Parallel Processing: Workshops, August 2019, ISBN:978-1-4503-7196-4

Proceedings of the 15th ACM International Conference on Computing Frontiers (CF 2018), ISBN:978-1-4503-5761-6