71,612 research outputs found
Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors
We introduce and experimentally validate a new macro-level model of the CPU
temperature/power relationship within nanometer-scale application processors or
system-on-chips. By adopting a holistic view, this model is able to take into
account many of the physical effects that occur within such systems. Together
with two algorithms described in the paper, our results can be used, for
instance by engineers designing power or thermal management units, to cancel
the temperature-induced bias on power measurements. This will help them gather
temperature-neutral power data while running multiple instance of their
benchmarks. Also power requirements and system failure rates can be decreased
by controlling the CPU's thermal behavior.
Even though it is usually assumed that the temperature/power relationship is
exponentially related, there is however a lack of publicly available physical
temperature/power measurements to back up this assumption, something our paper
corrects. Via measurements on two pertinent platforms sporting nanometer-scale
application processors, we show that the power/temperature relationship is
indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range.
Our data suggest that, for application processors operating between 20{\deg}C
and 50{\deg}C, a quadratic model is still accurate and a linear approximation
is acceptable.Comment: Submitted to SAMOS 2014; International Conference on Embedded
Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV
Ensemble Sales Forecasting Study in Semiconductor Industry
Sales forecasting plays a prominent role in business planning and business
strategy. The value and importance of advance information is a cornerstone of
planning activity, and a well-set forecast goal can guide sale-force more
efficiently. In this paper CPU sales forecasting of Intel Corporation, a
multinational semiconductor industry, was considered. Past sale, future
booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality
and other indicators were innovatively incorporated into the quantitative
modeling. Benefit from the recent advances in computation power and software
development, millions of models built upon multiple regressions, time series
analysis, random forest and boosting tree were executed in parallel. The models
with smaller validation errors were selected to form the ensemble model. To
better capture the distinct characteristics, forecasting models were
implemented at lead time and lines of business level. The moving windows
validation process automatically selected the models which closely represent
current market condition. The weekly cadence forecasting schema allowed the
model to response effectively to market fluctuation. Generic variable
importance analysis was also developed to increase the model interpretability.
Rather than assuming fixed distribution, this non-parametric permutation
variable importance analysis provided a general framework across methods to
evaluate the variable importance. This variable importance framework can
further extend to classification problem by modifying the mean absolute
percentage error(MAPE) into misclassify error. Please find the demo code at :
https://github.com/qx0731/ensemble_forecast_methodsComment: 14 pages, Industrial Conference on Data Mining 2017 (ICDM 2017
Comparing improved actions for SU(2)
In order to help the user in choosing the right action a performance
comparison is done for seven improved actions. Six of them are Symanzik
improved, one at tree-level and two at one-loop, all with or without tadpole
improvement. The seventh is an approximate fixed point action. Observables are
static on- and off-axis two-body potentials and four-body binding energies,
whose precision is compared when the same amount of computer time is used by
the programs.Comment: 3 pages, 3 colour eps figures. Presented at LATTICE9
A methodology for full-system power modeling in heterogeneous data centers
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener-
alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft
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