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International Symposium on parallel Architectures, Algorithms and Networks (ISPAN)

作者： Kuan-Ching Li Hsiao-Hsi Wang Chiou-Nan Chen Chun-Chieh Liu Chia-Fu Chang Chia-Wen Hsu Sheng-Shiang Hung Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan Laboratory of Bioinformatics and Computational Biology Department of Computer Science National Tsing Hua University Hsinchu Taiwan

The low-cost and availability of network of workstations have made them attractive solution for high performance computing. Striking progress of network technology in enabling high-performance global computing, with the utilization of cluster and grid technologies, in which computational and data resources in a local or wide area network are transparently employed to solve large-scale problems. In this paper, we present implementation and design rationale of Visuel toolkit for performance measurement and analysis of parallel applications, in cluster and grid environments. Most of performance visualization tools available today for high-performance platforms show solely system performance data (e.g., CPU load, memory usage, network bandwidth, server average load), and thus, being suitable for visualisation of computing platform system activities. The Visuel toolkit, is Web-based interface designed to show performance activities of all computing nodes of a cluster or grid computing platform involved in the execution of a parallel application, such as CPU load level and memory usage of each computing node. In addition, this toolkit is able to display comparative performance data visualizations generated from a number of executions of an application under investigation, analyzing the performance of different implementations. Evaluations using this toolkit show that it outperforms in easing the process of investigation and implementation of parallel applications, in effective way.

关键词： Monitoring Performance analysis Grid computing Computer networks Data visualization High performance computing Computer interfaces Concurrent computing Availability Workstations

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Locality Protected Dynamic Cache Allocation Scheme on GPUs

Locality Protected Dynamic Cache Allocation Scheme on GPUs

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yang Zhang Zuocheng Xing Li Zhou Chunsheng Zhu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Electronic Science and Engineering National University of Defense Technology Changsha China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781509032068

As we are approaching the exascale era in supercomputing, designing a balanced computer system with powerful computing ability and low energy consumption becomes increasingly important. GPU is a widely used accelerator in most recently applied supercomputers. It adopts massive multithreads to hide long latency and has high energy efficiency. In contrast to its strong computing power, GPUs have few on-chip resources with several MB of fast on-chip memory storage per SM (Streaming Multiprocessors). GPU caches exhibit poor efficiency due to the mismatch of the throughput-oriented execution model and its cache hierarchy design. Since the severe deficiency in on-chip memory, the benefit of high computing capacity of GPUs is pulled down by the poor cache performance dramatically, which limits system performance and energy-efficiency. In this paper, we put forward a locality protected scheme to make full use of the data locality based on the fixed capacity. We present a Locality Protected method based on instruction PC (LPP) to promote GPU performance. Firstly, we use a PC-based collector to collect the reuse information of each cache line. After getting the dynamic reuse information of the cache line, we take an intelligent cache allocation unit (ICAU) which coordinates the reuse information with LRU (Least Recently Used) replacement policy to find out the cache line with the least locality for eviction. The results show that LPP provides an up to 17.8% speedup and an average of 5.5% improvement over the baseline method.

关键词： Graphics processing units System-on-chip Computer architecture Instruction sets Resource management Optimization Electronic mail

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A peak performance model for Matrix multiplication on general-purpose DSP

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Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences 2013年第11 SUPPL.期40卷 148-152页

作者： Liu, Jie Chi, Li-Hua Xie, Lin-Chuan Wang, Yang Gan, Xin-Biao Feng, Hua Hu, Qing-Feng Science and Technology on Parallel and Distributed Processing Laboratory National Univ of Defense Technology Changsha Hunan 410073 China

DSP processor can be used to solve the high performance computation problems, which has the characteristics of high computing performance and low power. Matrix multiplication algorithm is the kernel of many scientific and technology computation, so it is of importance for theorem and practice. Based on general purpose DSP (GPDSP), a new parallel algorithm for matrix multiplication was proposed. And a peak performance model for matrix multiplication was built. From the peak performance model, an architecture of GPDSP was set up, and the parameter of GPDSP with Tflops was given, which includes the number of pipe-line, the number of SIMD registers, the breadth and latency for the hierarchical memories.

关键词： parallel algorithms

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A Runtime Verification Based Trace-Oriented Monitoring Framework for Cloud Systems

A Runtime Verification Based Trace-Oriented Monitoring Frame...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Jingwen Zhou Zhenbang Chen Ji Wang Zibin Zheng Wei Dong College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Cloud computing provides a new paradigm for resource utilization and sharing. However, the reliability problems, like system failures, often happen in cloud systems and bring enormous loss. Trace-oriented monitoring is an important runtime method to improve the reliability of cloud systems. In this paper, we propose to bring runtime verification into trace-oriented monitoring, to facilitate the specification of monitoring requirements and to improve the efficiency of monitoring cloud systems. Based on a data set collected from a cloud storage system in a real environment, we validate our approach by monitoring the critical properties of the storage system. The preliminary experimental results indicate the promise of our approach.

关键词： Monitoring Runtime Cloud computing Educational institutions Reliability Google Computers

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GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

arXiv

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

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Grid and Cooperative Computing - GCC 2004 Workshops 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Hai Jin Yi Pan Nong Xiao Jianhua Sun

ISBN: (数字)9783540302070

ISBN: (纸本)9783540235781

Welcome to the proceedings of GCC2004 and the city of Wuhan. Grid computing has become a mainstream research area in computer science and the GCC conference has become one of the premier forums for presentation of new and exciting research in all aspectsofgridandcooperativecomputing. Theprogramcommitteeispleasedtopresent the proceedings of the 3rd International Conference on Grid and Cooperative Comp- ing (GCC2004), which comprises a collection of excellent technical papers, posters, workshops, and keynote speeches. The papers accepted cover a wide range of exciting topics, including resource grid and service grid, information grid and knowledge grid, grid monitoring,managementand organizationtools, grid portal, grid service, Web s- vices and their QoS, service orchestration, grid middleware and toolkits, software glue technologies, grid security, innovative grid applications, advanced resource reservation andscheduling,performanceevaluationandmodeling,computer-supportedcooperative work, P2P computing, automatic computing, and meta-information management. The conference continues to grow and this year a record total of 581 manuscripts (including workshop submissions) were submitted for consideration. Expecting this growth, the size of the program committee was increased from 50 members for GCC 2003 for 70 in GCC 2004. Relevant differences from previous editions of the conf- ence: it is worth mentioning a signi?cant increase in the number of papers submitted by authors from outside China; and the acceptance rate was much lower than for p- vious GCC conferences. From the 427 papers submitted to the main conference, the program committee selected only 96 regular papers for oral presentation and 62 short papers for poster presentation in the program.

关键词： Theory of Computation Computer Communication Networks Operating Systems Information Systems Applications (incl. Internet) Information Storage and Retrieval User Interfaces and Human Computer Interaction

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Load Balancing a Multi-Block Grids-based Application on Heterogeneous Platform

Load Balancing a Multi-Block Grids-based Application on Hete...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Yonggang Che Chuanfu Xu Zhenghua Wang Institute for Quantum Information & State Key Lab. of High Performance Computing College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Lab National University of Defense Technology Changsha China

ISBN: (数字)9781665403986

ISBN: (纸本)9781665403993

This paper presents a load balancing method for a multi-block grids-based CFD (Computational Fluid Dynamics) application on heterogeneous platform. This method includes an asymmetric task scheduling scheme and a load balancing model. The idea is to balance the computing speed between the CPU and the coprocessor by adjusting the workload and the numbers of threads on both sides. Optimal load balance parameters are empirically selected, guided by a performance model. Performance evaluation is conducted on a computer server consists of two Intel Xeon E5-2670 v3 CPUs and two MIC coprocessors (Xeon Phi 5110P and Xeon Phi 7120P) for the simulation of turbulent combustion in a supersonic combustor. The results show that the performance is highly sensitive to the load balance parameters. With the optimal parameters, the heterogeneous computing achieves a maximum speedup of 2.30 × for a 6-block mesh, and a maximum speedup of 2.66 × for a 8-block mesh, over the CPU-only computing.

关键词： Computational modeling Computational fluid dynamics Load management Servers Task analysis Load modeling Coprocessors

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Multi-replica clustering management method based on limited-coding

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Ruan Jian Xue Bao/Journal of Software 2007年第6期18卷 1456-1467页

作者： Zhou, Jing Wang, Yi-Jie Li, Si-Kun School of Computer National University of Defense Technology Changsha 410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

In this paper, according to the resource management problems brought by a large number of replicas, a multi-replica clustering management method based on limited-coding is proposed. In this method, according to the process of creating new replicas from existent single replica, replicas are partitioned into different hierarchies and clusters. Then replicas are coded and managed based on the user-defined limited-coding rule consisting of replica hierarchy and replica sequence, which can also dispose the alteration of clusters caused by dynamic adjustments on replicas (replica addition or replica removal) effectively. After that, a management model of centralization in local and peer to peer in wide area is adopted to organize replicas, and the cost of reconciling consistency can be greatly depressed combining with defined minimal-time of update propagation. The relevance between the coding rule and the number of replicas, and the solutions to replica failure and replica recover are discussed. The results of the performance evaluation show that the clustering method is an efficient way to manage a large number of replicas, achieving good scalability, not sensitive to moderate node failure, and adapting well to applications with frequent updates.

关键词： Data processing

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An Energy-Efficient Scheduling Algorithm for Sporadic Real-Time Tasks in Multiprocessor Systems

An Energy-Efficient Scheduling Algorithm for Sporadic Real-T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Dong-Song Zhang Fang-Yuan Chen Hong-Hua Li Shi-Yao Jin De-Ke Guo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China School of Computing Science Simon Fraser University Burnaby BC Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha China

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.

关键词： Real time systems Scheduling algorithm Time frequency analysis Scheduling Heuristic algorithms Power demand

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