On the accuracy and usefulness of analytic energy models for contemporary multicore processors

This paper presents refinements to the execution-cache-memory performance model and a previously published power model for multicore processors. The combination of both enables a very accurate prediction of performance and energy consumption of contemporary multicore processors as a function of relevant parameters such as number of active cores as well as core and Uncore frequencies. Model validation is performed on the Sandy Bridge-EP and Broadwell-EP microarchitectures. Production-related variations in chip quality are demonstrated through a statistical analysis of the fit parameters obtained on one hundred Broadwell-EP CPUs of the same model. Insights from the models are used to explain the performance- and energy-related behavior of the processors for scalable as well as saturating (i.e., memory-bound) codes. In the process we demonstrate the models' capability to identify optimal operating points with respect to highest performance, lowest energy-to-solution, and lowest energy-delay product and identify a set of best practices for energy-efficient execution

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

The Phase A study of the ESA M4 mission candidate ARIEL

© 2018, The Author(s). ARIEL, the Atmospheric Remote sensing Infrared Exoplanet Large survey, is one of the three M-class mission candidates competing for the M4 launch slot within the Cosmic Vision science programme of the European Space Agency (ESA). As such, ARIEL has been the subject of a Phase A study that involved European industry, research institutes and universities from ESA member states. This study is now completed and the M4 down-selection is expected to be concluded in November 2017. ARIEL is a concept for a dedicated mission to measure the chemical composition and structure of hundreds of exoplanet atmospheres using the technique of transit spectroscopy. ARIEL targets extend from gas giants (Jupiter or Neptune-like) to super-Earths in the very hot to warm zones of F to M-type host stars, opening up the way to large-scale, comparative planetology that would place our own Solar System in the context of other planetary systems in the Milky Way. A technical and programmatic review of the ARIEL mission was performed between February and May 2017, with the objective of assessing the readiness of the mission to progress to the Phase B1 study. No critical issues were identified and the mission was deemed technically feasible within the M4 programmatic boundary conditions. In this paper we give an overview of the final mission concept for ARIEL as of the end of the Phase A study, from scientific, technical and operational perspectives. ispartof: Experimental Astronomy vol:46 issue:1 pages:211-239 status: publishe

Assessing natural ventilation rates using a combined measuring and modelling approach

Abstract Natural ventilation of animal houses clearly has advantages as for instance its low power consumption. However its application is often limited due to the lack of a reliable measuring and control system of the ventilation rate and so of emissions, as required for legislation. Although a lot of models exist to determine natural ventilation rates in buildings, it is still a challenge to know the ventilation rate accurately with few measurements. The objective of this work was to develop a model for the prediction of the natural ventilation rate in a pig house with as few measuring points as possible. Neural networks were used to investigate the reliability and accuracy of using as limited input as possible, taken from data collected from measurements with sonic anemometers in a real scale test building under outside weather conditions

Determination of a quantitative relationship between material properties, process settings and screw feeding behavior via multivariate data-analysis

In this study, a quantitative relationship between material properties, process settings and screw feeding responses of a high-throughput feeder was established via multivariate models (PLS). Thirteen divergent powders were selected and characterized for 44 material property descriptors. During volumetric feeder trials, the maximum feed capacity (FCCmax), the relative standard deviation on the maximum feed capacity (RSDFCmax), the short term variability (STRSD) and feed capacity decay (FCdecay) were determined. The gravimetric feeder trials generated values for the mass flow rate variability (RSDLC), short term variability (STRSD) and refill responses (V-refill and RSDrefill). The developed PLS models elucidated that the material properties and process settings were clearly correlated to the feeding behavior. The extended volumetric feeder trials pointed out that there was a significant influence of the chosen screw type and screw speed on the feeding process. Furthermore, the process could be optimized by reducing the feeding variability through the application of optimized mass flow filters, high frequency vibrations, independent agitator control and optimized top-up systems. Overall, the models could allow the prediction of the feeding performance for a wide range of materials based on the characterization of a subset of material properties greatly reducing the number of required feeding experiments

Latency-aware DVFS for Efficient Power State Transitions on Many-core Architectures

Energy efficiency is quickly becoming a first-class design constraint in high-performance computing (HPC). We need more efficient power management solutions to save energy costs and carbon footprint of HPC systems. Dynamic voltage and frequency scaling (DVFS) is a commonly used power management technique for making a trade-off between power consumption and system performance according to the time-varying program behavior. However, prior work on DVFS seldom takes into account the voltage and frequency scaling latencies, which we found to be a crucial factor determining the efficiency of the power management scheme. Frequent power state transitions without latency awareness can make a real impact on the execution performance of applications. The design of multiple voltage domains in some many-core architectures has made the effect of DVFS latencies even more significant. These concerns lead us to propose a new latency-aware DVFS scheme to adjust the optimal power state more accurately. Our main idea is to analyze the latency characteristics in depth and design a novel profile-guided DVFS solution which exploits the varying execution patterns of the parallel program to avoid excessive power state transitions. We implement the solution into a power management library for use by shared-memory parallel applications. Experimental evaluation on the Intel SCC many-core platform shows significant improvement in power efficiency after using our scheme. Compared with a latency-unaware approach, we achieve 24.0 % extra energy saving, 31.3 % more reduction in the energy---delay product and 15.2 % less overhead in execution time in the average case for various benchmarks. Our algorithm is also proved to outperform a prior DVFS approach attempted to mitigate the latency effects

The phase 0/A study of the ESA M3 mission candidate EChO

Stars and planetary system