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Research on Intelligent Maneuvering Decision Making Based on Deep Reinforcement Learning

Research on Intelligent Maneuvering Decision Making Based on...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Yao Wang Yi Jiang Huiqi Xu Yuan Zhang Naval Aviation University Yantai China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

At present, the maneuvering environment is complex and changeable, and unmanned platforms are faced with a variety of uncertain factors when carrying out multiple tasks. In order to improve the efficient and safe maneuvering performance of unmanned platforms in complex scenarios, this paper proposed building a maneuvering decision network system by using deep reinforcement learning network DDPG network for guiding agents execute maneuvering decisions according to tasks and environments to achieve the purpose of safe and efficient maneuvering. The paper tested the maneuvering decision performance in dynamic changing scenarios and verified the autonomous intelligent maneuvering performance of network guided agents, which could effectively avoid threats and choose better paths, and had good robustness and generalization.

关键词： Training Decision making Buildings parallel processing Deep reinforcement learning Robustness Network systems

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parallel, distributed, and Quantum Exact Single-Source Shortest Paths with Negative Edge Weights 32

Parallel, Distributed, and Quantum Exact Single-Source Short...

引用

32nd Annual European symposium on Algorithms, ESA 2024

作者： Ashvinkumar, Vikrant Bernstein, Aaron Cao, Nairen Grunau, Christoph Haeupler, Bernhard Jiang, Yonggang Nanongkai, Danupon Su, Hsin-Hao Rutgers University New BrunswickNJ United States Department of Computer Science Boston College Chestnut HillMA United States ETH Zürich Switzerland INSAIT Sofia Bulgaria Max Planck Institute for Informatics Saarland Germany Informatics Campus Saarbrücken Germany Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959773386

This paper presents parallel, distributed, and quantum algorithms for single-source shortest paths when edges can have negative integer weights (negative-weight SSSP). We show a framework that reduces negative-weight SSSP in all these settings to no(1) calls to any SSSP algorithm that works on inputs with non-negative integer edge weights (non-negative-weight SSSP) with a virtual source. More specifically, for a directed graph with m edges, n vertices, undirected hop-diameter D, and polynomially bounded integer edge weights, we show randomized algorithms for negative-weight SSSP with WSSSP (m, n)no(1) work and SSSSP (m, n)no(1) span, given access to a non-negative-weight SSSP algorithm with WSSSP (m, n) work and SSSSP (m, n) span in the parallel model, and TSSSP (n, D)no(1) rounds, given access to a non-negative-weight SSSP algorithm that takes TSSSP (n, D) rounds in CONGEST, and QSSSP (m, n)no(1) quantum edge queries, given access to a non-negative-weight SSSP algorithm that takes QSSSP (m, n) queries in the quantum edge query model. This work builds off the recent result of Bernstein, Nanongkai, Wulff-Nilsen [7], which gives a near-linear time algorithm for negative-weight SSSP in the sequential setting. Using current state-of-the-art non-negative-weight SSSP algorithms yields randomized algorithms for negative-weight SSSP with m1+o(1) work and n1/2+o(1) span in the parallel model, and (n2/5D2/5 + √n + D)no(1) rounds in CONGEST, and m1/2n1/2+o(1) quantum queries to the adjacency list or n1.5+o(1) quantum queries to the adjacency matrix. Up to a no(1) factor, the parallel and distributed results match the current best upper bounds for reachability [23, 12]. Consequently, any improvement to negative-weight SSSP in these models beyond the no(1) factor necessitates an improvement to the current best bounds for reachability. The quantum result matches the lower bound up to an no(1) factor [9]. Our main technical contribution is an efficient reduction from computing a l

关键词： Directed graphs

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Decentralized Low-Latency Task Scheduling for Ad-Hoc computing 35

Decentralized Low-Latency Task Scheduling for Ad-Hoc Computi...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Edinger, Janick Breitbach, Martin Gabrisch, Niklas Schafer, Dominik Becker, Christian Rizk, Amr Univ Hamburg Hamburg Germany Univ Mannheim Mannheim Germany Univ Duisburg Essen Duisburg Germany

ISBN: (纸本)9781665440660

End users can mutually share their computing resources in ad-hoc computing environments with code offloading. This augments the computational power of resource-constrained mobile devices and enables interactive user-facing applications that would otherwise exceed single device capabilities. However, ad-hoc computing comes along with new challenges such as heterogeneity and unreliability of devices. Resource consumers have to make task scheduling decisions without relying on a centralized scheduler to facilitate sub-second response times in environments with communication latencies that are in the order of the task execution limes. In this paper, we present a decentralized low-latency task scheduling approach that minimizes job execution times in heterogeneous ad-hoc environments. We propose two decentralized task scheduling algorithms that select powerful computing resources for parallel task execution while avoiding delays that arise from congested devices. We provide an analytical model of the performance of these algorithms before conducting an extensive evaluation based on real-world applications and a realistic computing infrastructure. Our results show that decentralized scheduling can dynamically adapt to varying system load and outperform a central scheduler in both task and job execution times, which enables low-latency task offloading in ad-hoc environments.

关键词： Performance evaluation Schedules Processor scheduling Scheduling algorithms Heuristic algorithms Probability Mobile handsets

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Genetic Algorithm Using Deep Learning Model for Priority Scheduling in Cloud-Fog Environment

Genetic Algorithm Using Deep Learning Model for Priority Sch...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zhan Zeng Xin Li Yue Hong Wei Zheng Department of Computer Science Xiamen University Xiamen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Cloud-fog architecture, as a hybrid distributed architecture, combines the powerful computing capabilities of cloud computing with the low-latency of fog computing, providing more efficient data processing services for Internet of Things (IoT) applications. In cloud-fog environment, efficient workflow scheduling algorithms are crucial to ensure the Quality of Service (QoS) of user applications, especially in terms of response time and energy consumption. To this end, this paper proposed an algorithm named Priority-Based Genetic Algorithm using deep learning model (PBGA). PBGA uses heuristic methods in the task sorting phase and introduces a modified genetic algorithm using a deep learning model to allocate resources to tasks. Additionally, the algorithm addresses resource overload by incorporating a task migration strategy. Experimental results indicate that compared to benchmark algorithms, PBGA achieves improvements of 3.5% to 57.4% in response time and 2.3% to 28.3% in energy consumption.

关键词： Deep learning Cloud computing Energy consumption Computational modeling Quality of service Computer architecture Scheduling Time factors Internet of Things Genetic algorithms

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distributed V2V computing Offloading Method Based on Delay and Fairness Awareness 19

Distributed V2V Computing Offloading Method Based on Delay a...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Yang, Guoliang Xiong, Mengqi Feng, Guangsheng Liu, Yuzheng Huang, Yun Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China China Aerosp Sci & Ind Corp Res Inst 2 Beijing Peoples R China

ISBN: (纸本)9781665435741

The roadside Basic unit (RSU) can relieve the heavy computing and analysis pressure required by vehicles'computing tasks. Due to the mobility of vehicles and limited computing resources, it is difficult to deal extensive vehicle tasks in time. Therefore, we consider the computation task offloading problem between the vehicles, as well as the dynamic topology caused by the vehicle movement and the randomness of task arrival, and propose an online offloading method based on fairness awareness, which can be ensured tolerable delays and the requirement of the quality of experience (QoE) between vehicles by introducing delay-aware and price-constrained virtual queues. By Lyapunov optimization technology, the objective function is transformed into a drift-plus-penalty profit minimum bound problem, achieveing all the offloading decisions by the realtime resource requirements and topology state. Finally, extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

关键词： Mobile computing offloading vehicle-to-vehicle communication delay tolerance fairness Lyapunov optimization

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Speedcode: Software Performance Engineering Education via the Coding of Didactic Exercises

Speedcode: Software Performance Engineering Education via th...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Tim Kaler Xuhao Chen Brian Wheatman Dorothy Curtis Bruce Hoppe Tao B. Schardl Charles E. Leiserson MIT CSAIL Johns Hopkins University Connective Associates

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, a task-parallel computing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.

关键词： distributed processing Multicore processing parallel programming Scalability Prototypes Software performance Encoding

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A Survey of Stochastic computing in Energy-Efficient DNNs On-Edge 19

A Survey of Stochastic Computing in Energy-Efficient DNNs On...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Wang, Danghui Wang, Zhaoqing Yu, Linfan Wu, Ying Yang, Jiaqi Mei, Kuizhi Wang, Jihe Northwestern Polytech Univ Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian Peoples R China Northwestern Polytech Univ Engn Res Ctr Embedded Syst Integrat Minist Educ Xian Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian 10049 Peoples R China

ISBN: (纸本)9781665435741

In recent years, "edge intelligent computing power deployment" has become a key and difficult problem in key areas such as system structure and artificial intelligence. Some independent decision-making scenario requires the edge devices to have the ability to handle high energy efficient and high throughput tasks. However, restricted by the traditional accurate computing mode and memory-computation separation structure, the edge devices often fail to provide the computing power guarantee of high throughput and high energy efficiency, which seriously hinders the edge intelligent deployment. This paper summarizes the new computing model integrating memory and computation of deploying stochastic circuits around memory, and constructs an edge intelligent system taking stochastic computing as a core from three levels of microstructure of circuit, architecture of near-memory stochastic computing and system-level software/hardware co-optimization. It strives to break through the power consumption and performant bottleneck of traditional computing model, and greatly improves the deploying ability of complex intelligent computing on edge equipment.

关键词： edge intelligent computing power stochastic computing memory-computation separation

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Brief Announcement: Relations Between Space-Bounded and Adaptive Massively parallel Computations 37

Brief Announcement: Relations Between Space-Bounded and Adap...

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37th international symposium on distributed computing, DISC 2023

作者： Chen, Michael Pavan, A. Vinodchandran, N.V. Iowa State University AmesIA United States University of Nebraska LincolnNE United States

ISBN: (纸本)9783959773010

In this work, we study the class of problems solvable by (deterministic) Adaptive Massively parallel Computations in constant rounds from a computational complexity theory perspective. A language L is in the class AMPC0 if, for every Ε > 0, there is a deterministic AMPC algorithm running in constant rounds with a polynomial number of processors, where the local memory of each machine s = O(NΕ). We prove that the space-bounded complexity class ReachUL is a proper subclass of AMPC0. The complexity class ReachUL lies between the well-known space-bounded complexity classes Deterministic Logspace (DLOG) and Nondeterministic Logspace (NLOG). In contrast, we establish that it is unlikely that PSPACE admits AMPC algorithms, even with polynomially many rounds. We also establish that showing PSPACE is a subclass of nonuniform-AMPC with polynomially many rounds leads to a significant separation result in complexity theory, namely PSPACE is a proper subclass of EXPΣP2 © Michael Chen, A. Pavan, and N. V. Vinodchandran;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Performance Analysis of Scientific computing Workloads on General Purpose TEEs 35

Performance Analysis of Scientific Computing Workloads on Ge...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Akram, Ayaz Giannakou, Anna Akella, Venkatesh Lowe-Power, Jason Peisert, Sean Univ Calif Davis Davis CA 95616 USA LBNL Berkeley CA USA

ISBN: (纸本)9781665440660

Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees. To study the applicability of hardware-based trusted execution environments (TEEs) to enable secure scientific computing, we deeply analyze the performance impact of general purpose TEEs, AMU SEV, and Intel SGX, for diverse HPC benchmarks including traditional scientific computing, machine learning, graph analytics, and emerging scientific computing workloads. We observe three main findings: 1) SEV requires careful memory placement on large scale NUMA machines (1x3.4x slowdown without and 1x-1.15x slowdown with NUMA aware placement), 2) virtualization-a prerequisite for SEV- results in performance degradation for workloads with irregular memory accesses and large working sets (1x-4x slowdown compared to native execution for graph applications) and 3) SGX is inappropriate for HPC given its limited secure memory size and inflexible programming model (1.2x-126x slowdown over unsecure execution). Finally, we discuss forthcoming new TEE designs and their potential impact on scientific computing.

关键词： HPC Hardware Security TEE SGX SEV

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distributed, Continuous and Real-time Trajectory Similarity Search

Distributed, Continuous and Real-time Trajectory Similarity ...

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international symposium on parallel and distributed computing

作者： Salman Ahmed Shaikh Hiroyuki Kitagawa Akiyoshi Matono Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

Moving objects’ trajectory data is becoming increasingly available with the omnipresent GPS-equipped devices, e.g., smart phones, smart watches, etc. Identification of similar trajectories is a fundamental requirement of many real-world applications, for instance, car pooling, road planning, epidemic contact tracing. There exist a number of studies focusing on trajectory similarity search, where given a query trajectory, similar trajectories are searched from a trajectory store. GPS devices generate trajectories as a continuous data stream, highlighting the critical need for real-time and continuous trajectory similarity search. Thus, given a trajectories stream ($S_{\Gamma}$) and a trajectory store (R), we propose a distributed, continuous and real-time similarity search between $S_{\Gamma}$ and R. The proposed method employs an index-based strategy to facilitate effective similarity search, with the added capability of execution in distributed environments to ensure scalable processing. A comprehensive empirical study is provided to demonstrate the efficiency and pruning capability of the proposed approach.

关键词： Epidemics Roads Focusing Real-time systems Trajectory Planning Object recognition distributed computing Smart phones Global Positioning System

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