In the last decade, there has been a significant upsurge in the demand for artificial intelligence. This remarkable growth can be attributed to the advancements in machine and deep learning techniques, coupled with th...
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Introducing undergraduate students to key concepts of distributed computing has become almost essential as the world continues to embrace cloud-based solutions to daily problems and as research continues to grow in sc...
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ISBN:
(纸本)9781665473668
Introducing undergraduate students to key concepts of distributed computing has become almost essential as the world continues to embrace cloud-based solutions to daily problems and as research continues to grow in scale requiring distributed resources. Although distributed computing is an important part of the computer science curriculum, it can be difficult to introduce at some institutions. We explore some key challenges associated with introducing distributed computing into the computer science curriculum at a small, liberal arts college. We focus on an initial failure introducing a specialized distributed computing course too soon and relay the successes and failures experienced over a one year span of incorporating key distributed computing concepts across multiple systems-level courses. We discuss lessons learned from our first foray into teaching distributed computing and provide recommendations for new adopters of distributed computing curriculum based on our experiences.
The paper provides an introduction into the theoretical expressiveness of graph neural networks. We discuss the basic properties and main applications of standard GNN models, and we show how these constructions are bo...
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ISBN:
(纸本)9783030948764;9783030948757
The paper provides an introduction into the theoretical expressiveness of graph neural networks. We discuss the basic properties and main applications of standard GNN models, and we show how these constructions are both upper and lower bounded in expressive power by the Weisfeiler-Lehman test. We then outline a wide variety of approaches to increase the expressiveness of GNNs above this theoretical limit, and discuss the strengths and weaknesses of these methods.
Procedural terrain generation is becoming more widespread with numerous applications in different domains, like video games, movies, and simulations. As the need for content size, quantity, and fidelity rise, old meth...
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ISBN:
(纸本)9798350334319
Procedural terrain generation is becoming more widespread with numerous applications in different domains, like video games, movies, and simulations. As the need for content size, quantity, and fidelity rise, old methods become insufficient to perform the task. The planetary scale also imposes new limitations on data continuity and uniqueness. This paper proposes a novel cloud-based planetoid terrain generation system based on software agents. The performance is also improved through vertical parallelism using Morsels and horizontal parallelism using multithreading. The workers processing generation requests are spread per cloud computation instance, so they can be further scaled as the number of requests increases.
In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pi...
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In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.
In this paper, we propose an Android-based distributed computing framework for accelerating DNN inference on Android edge devices. We experimentally demonstrate that the proposed distributed framework can reduce CPU u...
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ISBN:
(纸本)9781665486118
In this paper, we propose an Android-based distributed computing framework for accelerating DNN inference on Android edge devices. We experimentally demonstrate that the proposed distributed framework can reduce CPU utilization by 24% (making the the CPU utilization close to that of idle status), reduce power consumption by 59.8% to 71.8%, without leading to high-bandwidth througput. The proposed framework can be applied to various Android devices to enable cooperation among edge devices in a distributed computing manner, accelerate DNN inference, and enrich the functionality of Android devices to enhance user experience.
We study different parallelization schemes for the stochastic dual dynamic programming (SDDP) algorithm. We propose a taxonomy for these parallel algorithms, which is based on the concept of parallelizing by scenario ...
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We study different parallelization schemes for the stochastic dual dynamic programming (SDDP) algorithm. We propose a taxonomy for these parallel algorithms, which is based on the concept of parallelizing by scenario and parallelizing by node of the underlying stochastic process. We develop a synchronous and asynchronous version for each configuration. The parallelization strategy in the parallelscenario configuration aims at parallelizing the Monte Carlo sampling procedure in the forward pass of the SDDP algorithm, and thus generates a large number of supporting hyperplanes in parallel. On the other hand, the parallel-node strategy aims at building a single hyperplane of the dynamic programming value function in parallel. The considered algorithms are implemented using Julia and JuMP on a high performance computing cluster. We study the effectiveness of the methods in terms of achieving tight optimality gaps, as well as the scalability properties of the algorithms with respect to an increasing number of CPUs. In particular, we study the effects of the different parallelization strategies on performance when increasing the number of Monte Carlo samples in the forward pass, and demonstrate through numerical experiments that such an increase may be harmful. Our results indicate that a parallel-node strategy presents certain benefits as compared to a parallel-scenario configuration.
This paper studies the heterogeneous coded distributed computing (CDC) where input files required for job access have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of ...
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ISBN:
(数字)9781538683477
ISBN:
(纸本)9781538683477
This paper studies the heterogeneous coded distributed computing (CDC) where input files required for job access have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files and a nested coded shuffling strategy to effectively explore coded multicasting opportunities. We then formulate the joint optimization of the proposed file placement strategy and shuffling design variables into a mixed-integer linear programming (MILP) problem. To reduce the computational complexity, we propose a simple two-file-group-based approach to obtain an approximate solution. Numerical results show that the proposed two-file-group-based approach achieves nearly the same performance as solving the MILP problem using the conventional branch-and-cut method but with substantially lower computational complexity.
Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structur...
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Artificial neural networks (ANNs) are increasingly effective in addressing complex scientific and technological challenges. However, challenges persist in synthesizing neural network models and defining their structural parameters. This study investigates the use of parallel evolutionary algorithms on distributed computing systems (DCSs) to optimize energy consumption and computational time. New mathematical models for DCS performance and reliability are proposed, based on a mass service system framework, along with a multi-criteria optimization model designed for resource-intensive computational problems. This model employs a multi-criteria GA to generate a diverse set of Pareto-optimal solutions. Additionally, a decision-support system is developed, incorporating the multi-criteria GA, allowing for customization of the genetic algorithm (GA) and the construction of specialized ANNs for specific problem domains. The application of the decision-support system (DSS) demonstrated performance of 1220.745 TFLOPS and an availability factor of 99.03%. These findings highlight the potential of the proposed DCS framework to enhance computational efficiency in relevant applications.
distributed computing networks, tasked with both packet transmission and processing, require the joint optimization of communication and computation resources. We develop a dynamic control policy that determines both ...
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distributed computing networks, tasked with both packet transmission and processing, require the joint optimization of communication and computation resources. We develop a dynamic control policy that determines both routes and processing locations for packets upon their arrival at a distributed computing network. The proposed policy, referred to as Universal computing Network Control (UCNC), guarantees that packets i) are processed by a specified chain of service functions, ii) follow cycle-free routes between consecutive functions, and iii) are delivered to their corresponding set of destinations via proper packet duplications. UCNC is shown to be throughput-optimal for any mix of unicast and multicast traffic, and is the first throughput-optimal policy for non-unicast traffic in distributed computing networks with both communication and computation constraints. Moreover, simulation results suggest that UCNC yields substantially lower average packet delay compared with existing control policies for unicast traffic.
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