On Dimension Reduction for the Power Control Problem

In this paper we show how the dimension of power control equation systems can be reduced from K, the number of users in the system, to M, the number of cells, without any loss of generality in the analysis. Decentralized downlink power control algorithms are then presented which generalize previously proposed ones, broadening the range of application while maintaining reduced complexity

BSLD threshold driven power management policy for HPC centers

In this paper, we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in power dissipation and accordingly in energy consumption. However, lower frequencies introduce a penalty in performance. Our frequency assignment algorithm has two adjustable parameters in order to enable fine grain energy-performance trade-off control. Furthermore, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors for same load can lead to better energy efficiency and performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%– 18% on average depending on allowed job performance penalty. Using the power-aware job scheduling for 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Peer ReviewedPostprint (published version

BSLD threshold driven parallel job scheduling for energy efficient HPC centers

Recently, power awareness in high performance computing (HPC) community has increased significantly. While CPU power reduction of HPC applications using Dynamic Voltage Frequency Scaling (DVFS) has been explored thoroughly, CPU power management for large scale parallel systems at system level has left unexplored. In this paper we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. Traditional parallel job schedulers determine when a job will be run, power aware ones should assign CPU frequency which it will be run at. We have introduced two adjustable thresholds in order to enable fine grain energy performance trade-off control. Since our power reduction approach is policy independent it can be added to any parallel job scheduling policy. Furthermore, we have done an analysis of HPC system dimension. Running an application at lower frequency on more processors can be more energy efficient than running it at the highest CPU frequency on less processors. This paper investigates whether having more DVFS enabled processors and same load can lead to better energy efficiency and performance. Five workload logs from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%- 18% on average depending on allowed job performance penalty. Applying the same frequency scaling algorithm on 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.Postprint (published version

Numerical study on active and passive trailing edge morphing applied to a multi-MW wind turbine section

A progressive increasing in turbine dimension has characterized the technological development in offshore wind energy utilization. This aspect reflects on the growing in blade length and weight. For very large turbines, the standard control systems may not be optimal to give the best performance and the best vibratory load damping, keeping the condition of maximum energy production. For this reason, some new solutions have been proposed in research. One of these is the possibility of morphs the blade surface in an active way (increasing the performance in low wind region) or passive (load reduction) way. In this work, we present a numerical study on the active and passive trailing edge morphing, applied to large wind turbines. In particular, the study focuses on the aerodynamic response of a midspan blade section, in terms of fluid structure interaction (FSI) and driven surface deformation. We test the active system in a simple start-up procedure and the passive system in a power production with turbulent wind conditions, that is, two situations in which we expect these systems could improve the performance. All the computations are carried out with a FSI code, which couples a 2D-CFD solver, a moving mesh solver (both implemented in OpenFOAM library) and a FEM solver. We evaluate all the boundary conditions to apply in the section problem by simulating the 5MW NREL wind turbine with the NREL CAE-tools developed for wind turbine simulation

Deep reinforcement learning for active control of a three-dimensional bluff body wake

The application of deep reinforcement learning (DRL) to train an agent capable of learning control laws for pulsed jets to manipulate the wake of a bluff body is presented and discussed. The work has been performed experimentally at a value of the Reynolds number Re similar to 10(5) adopting a single-step approach for the training of the agent. Two main aspects are targeted: first, the dimension of the state, allowing us to draw conclusions on its effect on the training of the neural network; second, the capability of the agent to learn optimal strategies aimed at maximizing more complex tasks identified with the reward. The agent is trained to learn strategies that minimize drag only or minimize drag while maximizing the power budget of the fluidic system. The results show that independently on the definition of the reward, the DRL learns forcing conditions that yield values of drag reduction that are as large as 10% when the reward is based on the drag minimization only. On the other hand, when also the power budget is accounted for, the agent learns forcing configurations that yield lower drag reduction (5%) but characterized by large values of the efficiency. A comparison between the natural and the forced conditions is carried out in terms of the pressure distribution across the model's base. The different structure of the wake that is obtained depending on the training of the agent suggests that the possible forcing configuration yielding similar values of the reward is local minima for the problem. This represents, to the authors' knowledge, the first application of a single-step DRL in an experimental framework at large values of the Reynolds number to control the wake of a three-dimensional bluff body. Published under an exclusive license by AIP Publishing

Feature selection and classification of imbalanced datasets. Application to PET images of children with Autistic Spectrum Disorders

Learning with discriminative methods is generally based on minimizing themisclassification of training samples, which may be unsuitable for imbalanceddatasets where the recognition might be biased in favor of the most numerousclass. This problem can be addressed with a generative approach, which typicallyrequires more parameters to be determined leading to reduced performances inhigh dimension. In such situations, dimension reduction becomes a crucial issue.We propose a feature selection / classification algorithm based on generativemethods in order to predict the clinical status of a highly imbalanced datasetmade of PET scans of forty-five low-functioning children with autism spectrumdisorders (ASD) and thirteen non-ASD low-functioning children. ASDs aretypically characterized by impaired social interaction, narrow interests, andrepetitive behaviours, with a high variability in expression and severity. Thenumerous findings revealed by brain imaging studies suggest that ASD isassociated with a complex and distributed pattern of abnormalities that makesthe identification of a shared and common neuroimaging profile a difficult task.In this context, our goal is to identify the rest functional brain imagingabnormalities pattern associated with ASD and to validate its efficiency inindividual classification. The proposed feature selection algorithm detected acharacteristic pattern in the ASD group that included a hypoperfusion in theright Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateralpostcentral area. Our algorithm allowed for a significantly accurate (88\%),sensitive (91\%) and specific (77\%) prediction of clinical category. For thisimbalanced dataset, with only 13 control scans, the proposed generativealgorithm outperformed other state-of-the-art discriminant methods. The highpredictive power of the characteristic pattern, which has been automaticallyidentified on whole brains without any priors, confirms previous findingsconcerning the role of STS in ASD. This work offers exciting possibilities forearly autism detection and/or the evaluation of treatment response in individualpatients

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system

General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC

In this paper we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design