Understanding the Energy Consumption of HPC Scale Artificial Intelligence

International audienceThis paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a benchmark tool to evaluate the speed and energy consumption of DL algorithms in HPC environments. We exploited hardware counters and Python libraries to collect energy information through software, which enabled us to instrument a known AI benchmark tool, and to evaluate the energy consumption of numerous DL algorithms and models. Through an experimental campaign, we show a case example of the potential of benchmark-tracker to measure the computing speed and the energy consumption for training and inference DL algorithms, and also the potential of Benchmark-Tracker to help better understanding the energy behavior of DL algorithms in HPC platforms. This work is a step forward to better understand the energy consumption of Deep Learning in HPC, and it also contributes with a new tool to help HPC DL developers to better balance the HPC infrastructure in terms of speed and energy consumption

Obtaining Dynamic Scheduling Policies with Simulation and Machine Learning

International audienceDynamic scheduling of tasks in large-scale HPC platforms is normally accomplished using ad-hoc heuristics, based on task characteristics, combined with some backfilling strategy. Defining heuristics that work efficiently in different scenarios is a difficult task, specially when considering the large variety of task types and platform architectures. In this work, we present a methodology based on simulation and machine learning to obtain dynamic scheduling policies. Using simulations and a workload generation model, we can determine the characteristics of tasks that lead to a reduction in the mean slowdown of tasks in an execution queue. Modeling these characteristics using a nonlinear function and applying this function to select the next task to execute in a queue dramatically improved the mean task slowdown in synthetic workloads. When applied to real workload traces from highly different machines, these functions still resulted in important performance improvements, attesting the generalization capability of the obtained heuristics

Oscilador LC monolítico comandado por tensão a 2,4GHz

Esta Comunicação tem como finalidade divulgar o projecto de um VCO monolítico a 2.4GHz para integrar uma Malha de Captura de Fase (PLL). O Oscilador projectado é baseado num par diferencial cruzado (parte activa). O circuito funciona com uma tensão de 2.8V e com uma tensão de comando entre 1.6V e 1.8V, produzindo uma variação de frequência entre 2.4GHz e 2.75GHz.info:eu-repo/semantics/publishedVersio

HOMOGENIZATION METHOD FOR 2-D NANOSTRUCTURE REINFORCED EPOXY

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Short-Term Ambient Temperature Forecasting for Smart Heaters

Maintaining Cloud data centers is a worrying challenge in terms of energy efficiency. This challenge leads to solutions such as deploying Edge nodes that operate inside buildings without massive cooling systems. Edge nodes can act assmart heaters by recycling their consumed energy to heat these buildings. We propose a novel technique to perform temperature forecasting for Edge Computing smart heater environments. Our approach uses time series algorithms to exploit historical air temperature data with smart heaters’ power consumption and heat-sink temperatures to create models to predict short-term ambient temperatures. We implemented our approach on top of Facebook’s Prophet time series forecasting framework, and we used the real-time logs from Qarnot Computing as a usecase of a smart heater Edge platform. Our best trained model yields ambient temperature forecasts with less than 2.66% Mean Absolute Percentage Error showing the feasibility of near realtime forecasting