12,472 research outputs found
Modeling Energy Consumption of High-Performance Applications on Heterogeneous Computing Platforms
Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.
Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship between workload and power is less understood. Thus, investigation and analysis of power measurements has been a focal point in this work with the aim to improve the general understanding of energy consumption in the context of HPC.
This dissertation investigates power and energy consumption of many different parallel applications on several hardware platforms while varying a number of execution characteristics. Multicore and manycore hardware devices are investigated in homogeneous and heterogeneous computing environments. Further, common techniques for reducing power and energy consumption are employed to each of these devices.
Well-known power and performance models have been combined to form the Execution-Phase model, which may be used to quantify energy contributions based on execution phase and has been used to predict energy consumption to within 10%. However, due to limitations in the measurement procedure, a less intrusive approach is required.
The Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform analysis technique has been applied in innovative ways to model, analyze, and visualize power and energy measurements. EMD is widely used in other research areas, including earthquake, brain-wave, speech recognition, and sea-level rise analysis and this is the first it has been applied to power traces to analyze the complex interactions occurring within HPC systems.
Probability distributions may be used to represent power and energy traces, thereby providing an alternative means of predicting energy consumption while retaining the fact that power is not constant over time. Further, these distributions may be used to define the cost of a workload for a given computing platform
The Penn Jerboa: A Platform for Exploring Parallel Composition of Templates
We have built a 12DOF, passive-compliant legged, tailed biped actuated by
four brushless DC motors. We anticipate that this machine will achieve varied
modes of quasistatic and dynamic balance, enabling a broad range of locomotion
tasks including sitting, standing, walking, hopping, running, turning, leaping,
and more. Achieving this diversity of behavior with a single under-actuated
body, requires a correspondingly diverse array of controllers, motivating our
interest in compositional techniques that promote mixing and reuse of a
relatively few base constituents to achieve a combinatorially growing array of
available choices. Here we report on the development of one important example
of such a behavioral programming method, the construction of a novel monopedal
sagittal plane hopping gait through parallel composition of four decoupled 1DOF
base controllers.
For this example behavior, the legs are locked in phase and the body is
fastened to a boom to restrict motion to the sagittal plane. The platform's
locomotion is powered by the hip motor that adjusts leg touchdown angle in
flight and balance in stance, along with a tail motor that adjusts body shape
in flight and drives energy into the passive leg shank spring during stance.
The motor control signals arise from the application in parallel of four
simple, completely decoupled 1DOF feedback laws that provably stabilize in
isolation four corresponding 1DOF abstract reference plants. Each of these
abstract 1DOF closed loop dynamics represents some simple but crucial specific
component of the locomotion task at hand. We present a partial proof of
correctness for this parallel composition of template reference systems along
with data from the physical platform suggesting these templates are anchored as
evidenced by the correspondence of their characteristic motions with a suitably
transformed image of traces from the physical platform.Comment: Technical Report to Accompany: A. De and D. Koditschek, "Parallel
composition of templates for tail-energized planar hopping," in 2015 IEEE
International Conference on Robotics and Automation (ICRA), May 2015. v2:
Used plain latex article, correct gap radius and specific force/torque
number
Prediction and optimization techniques for performance enhancement of vehicular ad-hoc networks
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Wavelet-based response spectrum compatible synthesis of accelerograms-Eurocode application (EC8)
An integrated approach for addressing the problem of synthesizing artificial seismic accelerograms compatible with a given displacement design/target spectrum is presented in conjunction with aseismic design applications. Initially, a stochastic dynamics solution is used to obtain a family of simulated non-stationary earthquake records whose response spectrum is on the average in good agreement with the target spectrum. The degree of the agreement depends significantly on the adoption of an appropriate parametric evolutionary power spectral form, which is related to the target spectrum in an approximate manner. The performance of two commonly used spectral forms along with a newly proposed one is assessed with respect to the elastic displacement design spectrum defined by the European code regulations (EC8). Subsequently, the computational versatility of the family of harmonic wavelets is employed to modify iteratively the simulated records to satisfy the compatibility criteria for artificial accelerograms prescribed by EC8. In the process, baseline correction steps, ordinarily taken to ensure that the obtained accelerograms are characterized by physically meaningful velocity and displacement traces, are elucidated. Obviously, the presented approach can be used not only in the case of the EC8, for which extensive numerical results/examples are included, but also for any code provisions mandated by regulatory agencies. In any case, the presented numerical results can be quite useful in any aseismic design process dominated by the EC8 specifications
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Despite the widespread practical success of deep learning methods, our
theoretical understanding of the dynamics of learning in deep neural networks
remains quite sparse. We attempt to bridge the gap between the theory and
practice of deep learning by systematically analyzing learning dynamics for the
restricted case of deep linear neural networks. Despite the linearity of their
input-output map, such networks have nonlinear gradient descent dynamics on
weights that change with the addition of each new hidden layer. We show that
deep linear networks exhibit nonlinear learning phenomena similar to those seen
in simulations of nonlinear networks, including long plateaus followed by rapid
transitions to lower error solutions, and faster convergence from greedy
unsupervised pretraining initial conditions than from random initial
conditions. We provide an analytical description of these phenomena by finding
new exact solutions to the nonlinear dynamics of deep learning. Our theoretical
analysis also reveals the surprising finding that as the depth of a network
approaches infinity, learning speed can nevertheless remain finite: for a
special class of initial conditions on the weights, very deep networks incur
only a finite, depth independent, delay in learning speed relative to shallow
networks. We show that, under certain conditions on the training data,
unsupervised pretraining can find this special class of initial conditions,
while scaled random Gaussian initializations cannot. We further exhibit a new
class of random orthogonal initial conditions on weights that, like
unsupervised pre-training, enjoys depth independent learning times. We further
show that these initial conditions also lead to faithful propagation of
gradients even in deep nonlinear networks, as long as they operate in a special
regime known as the edge of chaos.Comment: Submission to ICLR2014. Revised based on reviewer feedbac
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