Exascale computing represents the next leap in the HPC race. Reaching this level of performance is subject to several engineering challenges such as energy consumption, equipmentcooling, reliability and massive parallelism. Model-based optimization is an essential tool in the design process and control of energy efficient, reliable and thermally constrained systems. However, in the Exascale domain, model learning techniques tailored to the specific supercomputer require real measurements and must therefore handle and analyze a massive amount of data coming from the HPC monitoring infrastructure. This becomes rapidly a "big data" scale problem. The common approach where measurements are first stored in large databases and then processed is no more affordable due to the increasingly storage costs and lack of real-time support. Nowadays instead, cloudbased machine learning techniques aim to build on-line models using real-time approaches such as "stream processing" and "inmemory" computing, that avoid storage costs and enable fastdata processing. Moreover, the fast delivery and adaptation of the models to the quick data variations, make the decision stage of the optimization loop more effective and reliable. In this paper we leverage scalable, lightweight and flexible IoT technologies, such as the MQTT protocol, to build a highly scalable HPC monitoring infrastructure able to handle the massive sensor data produced by next-gen HPC components. We then show how state-of-the art tools for big data computing and analysis, such as Apache Spark, can be used to manage the huge amount of data delivered by the monitoring layer and to build adaptive models in real-time using on-line machine learning techniques.

Energy efficiency and datacentre automation are critical targets of the research and deployment agenda of CINECA and its research partners in the Energy Efficient System Laboratory of the University of Bologna and the Integrated System Laboratory in ETH Zurich. In this manuscript, we present the primary outcomes of the research conducted in this domain and under the umbrella of several European, National and Private funding schemes. These outcomes consist of: (i) the ExaMon scalable, flexible, holistic monitoring framework, which is capable of ingesting 70GB/day of telemetry data of the entire CINECA datacentre and link this data with machine learning and artificial intelligence techniques and tools. (ii) The exploitation of ExaMon to evaluates the viability of machinelearning based job scheduling, power prediction and deep-learning based anomaly detection of compute nodes. (iii) The viability of scalable, out-of-band and high-frequency power monitoring in compute nodes, by leveraging low cost and open source embedded hardware and edge-computing, namely DiG. (iv) Finally, the viability of run time library to exploit communication regions in large-scale application to reduce the energy consumption without impairing the execution time, namely COUNTDOWN.

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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.

On the race toward exascale supercomputing systems are facing imall, power and energy consumption fueled by the end of Dennard's scaling start to show their impact on limiting supercomputers peak In this paper we present and describeanew methodology based power and aggregation of them for fast analysis and visualization. We propose a turn-key system which uses MQTT communication technology to measure and control power and performance. This methodology is shown as an integrated feature of the D.A.V.I.D.E. supercomputing machine.

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Power and thermal design and management are critical components of high performance computing (HPC) systems, due to their cutting-edge position in terms of high power density and large total power consumption. Many HPC power management strategies rely on the availability of accurate compact power models, capable of predicting power consumption and tracking its sensitivity to workload parameters and operating points. In this paper we describe a methodology and a framework for training two of the best-in-class power models directly on the online in production nodes and without requiring dedicated training instances. The compact power models are obtained using an online regression-based approach which can track non-stationary workloads and hardware variability. Our experiments on a real-life HPC system demonstrate that the models achieve very high accuracy over all operating modes. We also demonstrate the scalability of our approach and the small amount of resources needed for the online modeling, for both the training and inference phases.