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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.

Data centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance counters and power consumption are their key “vital signs”. State-of-the-Art (SoA) monitoring systems provide built-in tools to collect performance measurements, and custom solutions to get insight on their power consumption. However, with the increase in measurement resolution (in time and space) and the ensuing huge amount of measurement data to handle, new challenges arise, such as bottlenecks on the network bandwidth, storage and software overhead on the monitoring units. To face these challenges we propose a novel monitoring platform for data centers, which enables real-time high-resolution profiling (i.e., all available performance counters and the entire signal bandwidth of the power consumption at the plug—sampling up to 20 $$\upmu {\hbox {s}}$$ —with an error below 1%) and analytics, both at the edge (node-level analysis) and on a centralized unit (cluster-level analysis). The monitoring infrastructure is completely out-of-band, scalable, technology agnostic and low cost, and it is already installed in a SoA high-performance compute cluster (i.e., D.A.V.I.D.E. —18th in Green500 November 2017).

Fine-grain time synchronization is important to address several challenges in today and future High Performance Computing (HPC) centers. Among the many, (i) co-scheduling techniques in parallel applications with sensitive bulk synchronous workloads, (ii) performance analysis tools and (iii) autotuning strategies that want to exploit State-of-the-Art (SoA) high resolution monitoring systems, are three examples where synchronization of few microseconds is required. Previous works report custom solutions to reach this performance without incurring in extra cost of dedicated hardware. On the other hand, the benefits to use robust standards which are widely supported by the community, such as Network Time Protocol (NTP) and Precision Time Protocol (PTP), are evident. With today's software and hardware improvements of these two protocols and off-the-shelf integration in SoA HPC servers no expensive extra hardware is required anymore, but an evaluation of their performance in supercomputing clusters is needed. Our results show NTP can reach on computing nodes an accuracy of 2.6us and a precision below 2.7us, with negligible overhead. These values can be bounded below microseconds, with PTP and low-cost switches (no needs of GPS antenna). Both protocols are also suitable for data time-stamping in SoA HPC monitoring infrastructures. We validate their performance with two real use-cases, and quantify scalability and CPU overhead. Finally, we report software settings and low-cost network configuration to reach these high precision synchronization results., ACM International Conference Proceeding Series, ANDARE '18 Proceedings of the 2nd Workshop on AutotuniNg and aDaptivity AppRoaches for Energy efficient HPC Systems

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

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade&rsquo, s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.

Reliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution. During operation, several types of fault conditions or anomalies can arise, ranging from malfunctioning hardware to improper configurations or imperfect software. Currently, system administrator and final users have to discover it manually. Clearly this approach does not scale to large scale supercomputers and facilities: automated methods to detect faults and unhealthy conditions is needed. Our method uses a type of neural network called autoncoder trained to learn the normal behavior of a real, in-production HPC system and it is deployed on the edge of each computing node. We obtain a very good accuracy (values ranging between 90% and 95%) and we also demonstrate that the approach can be deployed on the supercomputer nodes without negatively affecting the computing units performance., Preprint of paper submitted and accepted AICAS2019 Conference (1st IEEE International Conference on Artificial Intelligence Circuits and Systems)

2016 International Conference on High Performance Computing & Simulation (HPCS 2016), ISBN:978-1-5090-2088-1, ISBN:978-1-5090-2089-8

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