974 research outputs found
3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems
Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity
Private Model Compression via Knowledge Distillation
The soaring demand for intelligent mobile applications calls for deploying
powerful deep neural networks (DNNs) on mobile devices. However, the
outstanding performance of DNNs notoriously relies on increasingly complex
models, which in turn is associated with an increase in computational expense
far surpassing mobile devices' capacity. What is worse, app service providers
need to collect and utilize a large volume of users' data, which contain
sensitive information, to build the sophisticated DNN models. Directly
deploying these models on public mobile devices presents prohibitive privacy
risk. To benefit from the on-device deep learning without the capacity and
privacy concerns, we design a private model compression framework RONA.
Following the knowledge distillation paradigm, we jointly use hint learning,
distillation learning, and self learning to train a compact and fast neural
network. The knowledge distilled from the cumbersome model is adaptively
bounded and carefully perturbed to enforce differential privacy. We further
propose an elegant query sample selection method to reduce the number of
queries and control the privacy loss. A series of empirical evaluations as well
as the implementation on an Android mobile device show that RONA can not only
compress cumbersome models efficiently but also provide a strong privacy
guarantee. For example, on SVHN, when a meaningful
-differential privacy is guaranteed, the compact model trained
by RONA can obtain 20 compression ratio and 19 speed-up with
merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1
Time-periodic solution to nonhomogeneous isentropic compressible Euler equations with time-periodic boundary conditions
In this paper, we study one-dimensional nonhomogeneous isentropic
compressible Euler equations with time-periodic boundary conditions. With the
aid of the energy methods, we prove the existence and uniqueness of the
time-periodic supersonic solutions after some certain time
Riemannian Gradient Algorithm for the Numerical Solution of Linear Matrix Equations
A Riemannian gradient algorithm based on geometric structures of a manifold consisting of all positive definite matrices is proposed to calculate the numerical solution of the linear matrix equation Q=X+∑i=1mAiTXAi. In this algorithm, the geodesic distance on the curved Riemannian manifold is taken as an objective function and the geodesic curve is treated as the convergence path. Also the optimal variable step sizes corresponding to the minimum value of the objective function are provided in order to improve the convergence speed. Furthermore, the convergence speed of the Riemannian gradient algorithm is compared with that of the traditional conjugate gradient method in two simulation examples. It is found that the convergence speed of the provided algorithm is faster than that of the conjugate gradient method
Global existence and stability of subsonic time-periodic solution to the damped compressible Euler equations in a bounded domain
In this paper, we consider the one-dimensional isentropic compressible Euler
equations with source term in a bounded domain,
which can be used to describe gas transmission in a nozzle.~The model is
imposed a subsonic time-periodic boundary condition.~Our main results reveal
that the time-periodic boundary can trigger an unique subsonic time-periodic
smooth solution and this unique periodic solution is stable under small
perturbations on initial and boundary data.~To get the existence of subsonic
time-periodic solution, we use the linear iterative skill and transfer the
boundary value problem into two initial value ones by using the hyperbolic
property of the system. Then the corresponding linearized system can be
decoupled.~The uniqueness is a direct by-product of the stability. There is no
small assumptions on the damping coefficient
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