Topology-aware Job Scheduling and Placement in High Performance Computing and Edge Computing Systems PDF Download
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Author: Kangkang Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
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Author: Kangkang Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 124
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Author: Yuping Fan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Yi Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 328
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The problem of job scheduling on Partitionable Massively Parallel Processor (PMPP) Systems is studied in this dissertation. The objective of the scheduling problem is to minimize the makespan or the total completion time. Key system features include the following: (1) the number of processors K is large relative to the number of jobs n (K $ge$ 2n), (2) the processing time of a job depends on the number of processors assigned to it, and (3) unlimited repartitioning of the processors is allowed. Three types of speedup functions are considered: linear, sublinear, and superlinear. While simple rules are found to solve problems with linear and sublinear speedup functions, the superlinear rate function, which is most common, leads to an NP-hard problem. A Processor Partition Algorithm (PP) based on the results of a continuous-resource scheduling problem is developed for this problem. It is proved that Algorithm PP finds the best schedule among all parallel schedules. Moreover, Algorithm PP has a tight worst case absolute performance bound of 1 + 1/$theta$ when the speedup function is concave and superlinear. ($theta$ = $Kover n$ $-$ 1) Several variants of Algorithm PP are developed. Simulation shows that significant performance improvement can be obtained by using these variants. PMPP systems with a mesh-topology are also considered. Extension of Algorithm PP to this topology indicates that the absolute worst case performance bound is also 1 + 1/$theta$. A hybrid 2-partition-Algorithm PP variant is shown to perform better than a one-dimension-fixed heuristic in simulation studies. More computational results are presented to show the effect of different parameters on the makespan. This dissertation also studies a high performance multiprocessor time-sharing computing system. A new job loading policy is developed for this system. The importance of using information on job resource requirements to prioritize and limit jobs admitted into the kernel is illustrated through simulation model. The simulation study is based on a stationary model of a computing system as well as on a model using nonstationary historical data from NCSA's Cray-YMP system.
Author: Michael J. North
Publisher:
ISBN:
Category :
Languages : en
Pages : 448
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Author: Carl K. Albing
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jaya Prakash Varma Champati
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Scheduling tasks/jobs on parallel processors/machines is a classical scheduling problem that is well studied in the literature due to its applicability in parallel computing systems and operations research. Even though it is a well studied problem, new scheduling models that consider the emerging aspects in the continuously evolving parallel computing systems are required to analyze and improve their performance. In this thesis we initially study scheduling on parallel processors under three new semi-online paradigms with motivations from computational offloading systems (e.g. mobile cloud computing, mobile edge computing, hybrid cloud, etc.), where only partial information about the tasks is available. First, we study makespan minimization on a set of local processors and a set of remote processors under the semi-online setting where the task processing times on the local processors are known a priori, but are unknown on the remote processors. Second, considering that each offloaded task incurs a cost, we study makespan-plus-weighted-offloading-cost minimization when the task processing times are not known a priori, but their offloading costs are known. Third, we consider the scenario where offloaded task incurs non-negligible communication overhead and study makespan minimization under the semi-online setting where task communication overheads are known a priori, but their processing times are not known. For each of the above problems we propose efficient algorithms, analyze their performance in the worst case, by deriving competitive ratios, and study their average performance using simulation. Finally, we identity a new job model in scheduling on parallel processors, where a job has placement constraints and multi-processor requirement. Under the job placement constraints we solve a problem of assigning identical jobs to machines. We establish novel results for this problem under generalized objective functions. We propose a new algorithm and analyze its runtime complexity. Further, using benchmark input instances we show that the proposed algorithm has orders of magnitude lower runtime than the existing algorithms.
Author: Ahmed AbdElHai ElRefae Ebaid
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Xinliang Wei
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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In recent years, edge computing has become an increasingly popular computing paradigm to enable real-time data processing and mobile intelligence. Edge computing allows computing at the edge of the network, where data is generated and distributed at the nearby edge servers to reduce the data access latency and improve data processing efficiency. In addition, with the advance of Artificial Intelligence of Things (AIoT), not only millions of data are generated from daily smart devices, such as smart light bulbs, smart cameras, and various sensors, but also a large number of parameters of complex machine learning models have to be trained and exchanged by these AIoT devices. Classical cloud-based platforms have difficulty communicating and processing these data/models effectively with sufficient privacy and security protection. Due to the heterogeneity of edge elements including edge servers, mobile users, data resources, and computing tasks, the key challenge is how to effectively manage resources (e.g. data, services) and schedule tasks (e.g. ML/FL tasks) in the edge clouds to meet the QoS of mobile users or maximize the platform's utility. To that end, this dissertation studies joint resource management and task scheduling for mobile edge computing. The key contributions of the dissertation are two-fold. Firstly, we study the data placement problem in edge computing and propose a popularity-based method as well as several load-balancing strategies to effectively place data in the edge network. We further investigate a joint resource placement and task dispatching problem and formulate it as an optimization problem. We propose a two-stage optimization method and a reinforcement learning (RL) method to maximize the total utilities of all tasks. Secondly, we focus on a specific computing task, i.e., federated learning (FL), and study the joint participant selection and learning scheduling problem for multi-model federated edge learning. We formulate a joint optimization problem and propose several multi-stage optimization algorithms to solve the problem. To further improve the FL performance, we leverage the power of the quantum computing (QC) technique and propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm as well as a multiple-cuts version to accelerate the convergence speed of the HQCBD algorithm. We show that the proposed algorithms can achieve the consistent optimal value compared with the classical Benders' decomposition running in the classical CPU computer, but with fewer convergence iterations.
Author: Laurence T. Yang
Publisher: Wiley-Interscience
ISBN:
Category : Computers
Languages : en
Pages : 824
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With hyperthreading in Intel processors, hypertransport links in next generation AMD processors, multi-core silicon in today's high-end microprocessors from IBM and emerging grid computing, parallel and distributed computers have moved into the mainstream.
Author: Georg Hager
Publisher: CRC Press
ISBN: 1439811938
Category : Computers
Languages : en
Pages : 350
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Book Description
Written by high performance computing (HPC) experts, Introduction to High Performance Computing for Scientists and Engineers provides a solid introduction to current mainstream computer architecture, dominant parallel programming models, and useful optimization strategies for scientific HPC. From working in a scientific computing center, the author
