This paper proposes a DNN-based distributed algorithm by deploying one DNN in each base station controller (BSC) to approximate the weighted minimum mean-square error (WMMSE) algorithm, which can allocate power in par...
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ISBN:
(纸本)9781728133164
This paper proposes a DNN-based distributed algorithm by deploying one DNN in each base station controller (BSC) to approximate the weighted minimum mean-square error (WMMSE) algorithm, which can allocate power in parallel. Maximizing the weighted sum-rate is a common problem setup for power allocation in a single-input single-output (SISO) interfering broadcast channel (IBC). The WMMSE algorithm is a suboptimal and classical solution but has high computational complexity. Recently, a DNN-based algorithm is proposed to speed up the power allocation by approximating the WMMSE algorithm. Motivated by these, a DNN-based distributed algorithm is proposed and the system with 7 cells and 56 users is setup. The experimental results show that centralized DNN with at least 7 layers can achieve the sum-rate performance of 95.02%, but distributed DNN with only 5 layers can achieve the sum-rate performance of 97.40%.
Large-scale donation-based distributed infrastructures need to cope with the inherent unreliability of participant nodes. A widely-used work scheduling technique in such environments is to redundantly schedule the out...
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ATLAS has recorded more than 8 petabyte(PB) of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration ...
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ATLAS has recorded more than 8 petabyte(PB) of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 90PB are currently stored in the Worldwide LHC computinggrid by ATLAS. All these data are managed by the ATLAS distributed Data Management system, called Don Quijote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS computing operations manage these large quantities of data across the many grid sites at which ATLAS runs, and to help ATLAS physicists get access to these data. In this paper, we describe new and improved DQ2 services, and the experience of data management operation in ATLAS computing, showing how these services enable the management of PB scale computing operations. We also present the concepts of the new version of the ATLAS distributed Data Management (DDM) system, Rucio.
This study presents a new algorithm and corresponding statistical package for estimating optimal bandwidth for a nonparametric kernel regression. Kernel regression is widely used in Economics, Statistics, and other fi...
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ISBN:
(纸本)9780769561493
This study presents a new algorithm and corresponding statistical package for estimating optimal bandwidth for a nonparametric kernel regression. Kernel regression is widely used in Economics, Statistics, and other fields. The formula for the optimal "bandwidth," or smoothing parameter, is well-known. In practice, however, the computational demands of estimating the optimal bandwidth have historically been prohibitively high. Consequently, researchers typically select bandwidths for kernel regressions using ad hoc rules of thumb. This paper exploits the Single Program Multiple Data (SPMD) parallelism inherent in optimal bandwidth calculation to develop a method for computing optimal bandwidth on a GPU. Using randomly generated datasets of different sizes, this approach is shown to reduce the run time by as much as a factor of seven.
In this study, a cluster-computing environment is employed as a computational platform. In order to increase the efficiency of the system, a dynamic task scheduling algorithm is proposed, which balances the load among...
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In this paper, we propose an algorithm: parallel Three-Phase Dependency Analysis (P-TPDA), for learning the structure of Bayesian Network from distributed homogenous datasets: each of which has same variables. The alg...
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ISBN:
(纸本)0769529097
In this paper, we propose an algorithm: parallel Three-Phase Dependency Analysis (P-TPDA), for learning the structure of Bayesian Network from distributed homogenous datasets: each of which has same variables. The algorithm has two steps: local learning and global learning. In local learning, we first obtain local Bayesian Networks on each dataset independently using Bayesian Network Power Constructor system. Then in global learning, we combine those local structures into the final structure with conditional independency (CI) test. The simulated experimental results for alarm networks indicate: when the number of records in dataset is more than 10000, the final structure obtained with P-TPDA algorithm is consistent with the structure obtained with centralized solution. But the running time in P-TPDA algorithm is shorter than the running time in centralized solution.
Cloud computing infrastructures and gridcomputing platforms are representatives of a new breed of systems that leverage the modularity paradigm to assemble large-scale dynamic applications from modules contributed by...
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The need for increased performance of mobile device directly conflicts with the desire for longer battery life. Offloading computation to multiple devices is an effective method to reduce energy consumption and enhanc...
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ISBN:
(纸本)9781479986767
The need for increased performance of mobile device directly conflicts with the desire for longer battery life. Offloading computation to multiple devices is an effective method to reduce energy consumption and enhance performance for mobile applications. Android provides mechanisms for creating mobile applications but lacks a native scheduling system for determining where code should be executed. This paper presents Jade, a system that adds sophisticated energy-aware computation offloading capabilities to Android apps. Jade monitors device and application status and automatically decides where code should be executed. Jade dynamically adjusts offloading strategy by adapting to workload variation, communication costs, and energy status in a distributed network of Android and non-Android devices. Jade minimizes the burden on developers to build applications with computation offloading ability by providing easy-to-use Jade API. Evaluation shows that Jade can effectively reduce up to 39% of average power consumption for mobile application while improving application performance.
A grid system can be considered as an infrastructure that allows location independent access to the resources and services that are provided by geographically distributed machines and networks. One fundamental operati...
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The Random Access, Visualization and Exploration Network (RAVEN) aims to allow for the storage, analysis and visualisation of peta-bytes of scientific data in (near) real-time. In essence, RAVEN is a huge distributed ...
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ISBN:
(纸本)9783642281440
The Random Access, Visualization and Exploration Network (RAVEN) aims to allow for the storage, analysis and visualisation of peta-bytes of scientific data in (near) real-time. In essence, RAVEN is a huge distributed and parallel system. While testing of distributed systems, such as huge telecommunication systems, is well understood and performed systematically, testing of parallel systems, in particular high-performance computing, is currently lagging behind and is mainly based on ad-hoc approaches. This paper surveys the state of the art of software testing and investigates challenges of testing a distributed and parallel high-performance RAVEN system. While using the standardised Testing and Test Control Notation (TTCN-3) looks promising for testing networking and communication aspects of RAVEN, testing the visualisation and analysis aspects of RAVEN may open new frontiers.
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