I review recent research and advances in algorithms for solvers and gauge generation, with an emphasis on practical algorithms for four dimensional simulations. Particular consideration is given to advances in multigr...
详细信息
The rise of the Internet of Things (IoT) has led to an increased risk of cyber attacks on IoT devices, including botnet attacks. Botnets are networks of compromised devices that can be controlled remotely by cybercrim...
详细信息
parallelcomputing and distributedcomputing are the popular terminologies of scheduling. With advancement in technology, systems have become much more compact and fast and need of parallelization plays a major role f...
详细信息
ISBN:
(纸本)9783031368042;9783031368059
parallelcomputing and distributedcomputing are the popular terminologies of scheduling. With advancement in technology, systems have become much more compact and fast and need of parallelization plays a major role for this compaction. Wireless computing is also a common concept associated with each new development. Scheduling of tasks has always been a challenging area and is an NP-complete problem. Moreover, when it comes to wireless distributedcomputing, reliable scheduling plays an important role in order to complete a task in a wireless distributed system. This work proposes an algorithm to dynamically schedule tasks on heterogeneous processors within a wireless distributedcomputing system. A lot of heuristics, meta-heuristics & genetics have been used earlier with scheduling strategies. However, most of them haven't taken reliability into account before scheduling. Here a heuristic that deals with reliable scheduling is considered. The scheduler also works within an environment which has dynamically changing resources and adapts itself to changing system resources. The testing was carried out with up to 200 tasks being scheduled while testing in a real time wireless distributed environment. Experiments have shown that the algorithm outperforms the other strategies and can achieve a better reliability along with no increase in make-span, in spite of wireless nodes.
Modeling urban mobility behaviours with micro-scopic traffic flow simulation is now crucial for studying intel-ligent urban decision-making algorithms, such as traffic light control and road congestion charging. Howev...
详细信息
Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU) aiming to reduce the processing delay as well as the resource con...
详细信息
ISBN:
(纸本)9781665473156
Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU) aiming to reduce the processing delay as well as the resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications resulting in suboptimal offloading policies. In this paper, we present FEVEC, a Fast and Energy-efficient VEC framework with the objective of making the optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirement. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decision and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC, where an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Furthermore, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on realistic and simulated vehicle trajectories verify that our proposed algorithm has a better performance compared with the state-of-the-art VEC mechanism.
This paper provides an overview on scalable deep learning platforms and how they are used in medical context. An introduction highlights the key factors, then an overview on medical context is provided. Afterwards, th...
详细信息
ISBN:
(纸本)9781450393867
This paper provides an overview on scalable deep learning platforms and how they are used in medical context. An introduction highlights the key factors, then an overview on medical context is provided. Afterwards, the basic concepts about deep learning and parallel and distributedcomputing are briefly recalled. Then a specific deep learning library for medical applications is described. The last part of the paper is focused on a real use case application of deep learning on medical data. As a result, the main contribution of this paper is a short survey on main scalable deep learning platforms with a first analysis of their features, and the description of a practical example.
Operating systems mediate user-device interactions, crucially managing resources, software execution, and application interfaces. In the face of technological advancements, the demand for secure, resilient, and scalab...
详细信息
Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two s...
详细信息
HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware re...
详细信息
Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we present our parallel Dynamic F...
详细信息
ISBN:
(纸本)9798350364613;9798350364606
Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we present our parallel Dynamic Frontier (DF) approach. Given a batch update of edge deletions or insertions, this approach incrementally identifies an approximate set of affected vertices in the graph with minimal overhead. We apply this approach to both Louvain, a high quality, and Label Propagation Algorithm (LPA), a fast static community detection algorithm. Our approach achieves a mean speedup of 7.3x and 6.7x, when applied to Louvain and LPA respectively, compared to our parallel and optimized implementation of Delta-screening, a recently proposed state-of-the-art approach. Finally, we show how to combine Louvain and LPA with the DF approach to arrive at a hybrid algorithm. This algorithm produces high-quality communities while providing a speedup of 2.0 x on top of DF-based Louvain.
暂无评论