Networks Of Workstations (NOWs) are attractive for parallel processing due to their cost advantage. This paper investigates the performance issues in processing join operations and the inherent tradeoff in the network...
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ISBN:
(纸本)0769505686
Networks Of Workstations (NOWs) are attractive for parallel processing due to their cost advantage. This paper investigates the performance issues in processing join operations and the inherent tradeoff in the networked workstation environment. Specifically, we look at the performance of the nested-loop join algorithm. Since NOWs are heterogeneous in nature, loan sharing is important for their performance. We evaluated the performance of three load sharing methods: static equal, static proportional, and dynamic scheduling with fixed-chunk size. The three scheduling methods are evaluated on an experimental heterogeneous network of workstations with non-query background loads. Our experimental results suggest that, when there is no background load, dynamic scheduling outperforms static equal scheduling (up to 40%) and marginally better (about 10% better speedup) than the static proportional scheduling. When there is dynamic background load on nodes, dynamic scheduling provides substantial performance improvement over the static proportional scheduling (up to 50%) and static equal scheduling (up to about 100%). In all cases, selection of an appropriate chunk size is important in dynamic scheduling.
Automated performance modeling and performance prediction of parallel programs are highly valuable in many use cases, such as in guiding task management and job scheduling, offering insights of application behaviors, ...
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ISBN:
(纸本)9781538637906
Automated performance modeling and performance prediction of parallel programs are highly valuable in many use cases, such as in guiding task management and job scheduling, offering insights of application behaviors, assisting resource requirement estimation, etc. The performance of parallel programs is affected by numerous factors, including but not limited to hardware, system software, applications, algorithms, and input parameters, thus an accurate performance prediction is often a challenging and daunting task. In this study, we focus on automatically predicting the execution time of parallel programs (more specifically, MPI programs) with different inputs, at different scale, and without domain knowledge. We model the correlation between the execution time and domain-independent runtime features. These features include values of variables, counters of branches, loops, and MPI communications. Through automatically instrumenting an MPI program, each execution of the program will output a feature vector and its corresponding execution time. After collecting data from executions with different inputs, a random forest machine learning approach is used to build an empirical performance model, which can predict the execution time of the program with a new input. Our experiments and analyses of three parallel programs, Graph500, GalaxSee and SMG2000, on three different systems show that our method performs well, with less than 20% error in predictions on average.
In this paper, we study a parallel job scheduling model which takes into account both computation time and the overhead from communication between processors. Assuming that a job Jj has a processing requirement pj and...
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ISBN:
(纸本)9780889867048
In this paper, we study a parallel job scheduling model which takes into account both computation time and the overhead from communication between processors. Assuming that a job Jj has a processing requirement pj and is assigned to kj processors for parallel execution, then the execution time will be modeled by tj = p j / kj+ (kj - 1) c, where c is the constant overhead cost associated with each processor other than the master processor. In this model, (kj - 1)c represents the cost for communication and coordination among the processors. This model attempts to accurately portray the actual execution time for jobs running in parallel on multiple processors. Using this model, we will study the online algorithm Earliest Completion Time (ECT) and show a lower bound for the competitive ratio of ECT for m ≥ 2 processors. For m ≤ 4, we show the matching upper bound to complete the competitive analysis for m = 2,3,4. For large m, we conjecture that the ratio approaches 30/13 ≈ 2.30769.
In this paper, a distributed fusion white noise deconvolution estimator is presented for the multisensor linear discrete systems with different measurement matrices and correlated measurement noises. It is globally op...
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ISBN:
(纸本)9781479913909
In this paper, a distributed fusion white noise deconvolution estimator is presented for the multisensor linear discrete systems with different measurement matrices and correlated measurement noises. It is globally optimal because it is derived from the centralized fusion white noise deconvolution estimator and is identical to the centralized fuser. The proposed white noise fuser is obtained based on the local Kalman predictors. Compared with the existing globally suboptimal distributed fusion white noise estimators, the computation of complex covariance matrices is avoided. The effectiveness of the proposed results is shown by a Monte Carlo simulation for the Bernoulli-Gaussian input white noise.
In this paper, we propose and study a novel distributed traffic information system. The contributions of the paper include a road-network-aware (RNA) information publication protocol, a distributed query processing pr...
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ISBN:
(纸本)9781424413959
In this paper, we propose and study a novel distributed traffic information system. The contributions of the paper include a road-network-aware (RNA) information publication protocol, a distributed query processing protocol with transient memory, and an adaptive interaction model between the two, all in the context of a distributed traffic surveillance infrastructure. Our study focuses on (1) the impact of various information demand characteristics on dissemination strategies, and (2) the adaptive optimal strategies in a distributed manner without prior knowledge of information demand characteristics. Both theoretical and simulation results are presented.
In this paper we describe a task allocation method, that utilizes genetic programming to find a suitable solution in an adequate time for this NP-complete combinatorial optimization problem. The underlying distributed...
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ISBN:
(纸本)9780769530499
In this paper we describe a task allocation method, that utilizes genetic programming to find a suitable solution in an adequate time for this NP-complete combinatorial optimization problem. The underlying distributed embedded system is heterogenous, consisting of different processors with different properties such as core type, clock frequency, available memory, and I/O interfaces, interconnected with different communication media. In our applications, which are described as data flow graphs, the number of tasks to be placed is much larger than the number of processors available. We highlight the difficulties when applying genetic programming to this problem and present our solutions and enhancements, accompanied with some simulation results.
Dynamic method replacement is a new technique to eliminate bottlenecks (e.g., around a root of tree structure) using adaptive objects for concurrent accesses. The technique eliminates the frequency of mutual exclusion...
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ISBN:
(纸本)0818685913
Dynamic method replacement is a new technique to eliminate bottlenecks (e.g., around a root of tree structure) using adaptive objects for concurrent accesses. The technique eliminates the frequency of mutual exclusion and remote message passing by dynamically increasing the number of read only methods and the immutable part of objects. The results of performance measurements our both shared memory and distributed memory parallel architectures indicate the effectiveness of our approach to bottleneck elimination.
In this paper, we propose a Modified distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probab...
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ISBN:
(纸本)9781479906529
In this paper, we propose a Modified distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. Tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.
This research is aimed at the implementation issue of an information security management system in distributedinformationsystems as an important requirement for increasing the level of information and cyber security...
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Simultaneous Wireless information and Power Transfer (SWIPT) technique is introduced in Radio Frequency (RF) communication to carry both information and power in same medium. In this approach, the energy can be harves...
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ISBN:
(纸本)9781728143514
Simultaneous Wireless information and Power Transfer (SWIPT) technique is introduced in Radio Frequency (RF) communication to carry both information and power in same medium. In this approach, the energy can be harvested while decoding the information carries in an RF wave. Recently, the same concept applied in Visible Light Communication (VLC) namely Simultaneous Light Wave information and Power Transfer (SLIPT), which is highly recommended in an indoor applications to overcome the problem facing in RF communication. Thus, SLIPT is introduced to transmit the power through a Light Emitting Diode (LED) luminaries. In this work, we compare both SWIPT and SLIPT technologies and realize SLIPT technology archives increased performance in terms of the amount of harvested energy, outage probability and error rate performance.
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