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丛书名： Lecture Notes in computer science

1000年

作者： Xiaotie Deng John E. Hopcroft Jinyun Xue

ISBN: (数字)9783642022708

ISBN: (纸本)9783642022692

This book constitutes the refereed proceedings of the Third International Frontiers of Algorithmics Workshop, FAW 2009, held in Hefei, Anhui, China, in June 2009. The 33 revised full papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on graph algorithms; game theory with applications; graph theory, computational geometry; machine learning; parameterized algorithms, heuristics and analysis; approximation algorithms; as well as pattern recognition algorithms, large scale data mining.

关键词： Data Structures Mathematics of computing Artificial Intelligence Algorithm Analysis and Problem Complexity Discrete Mathematics in computer science Software engineering/Programming and Operating Systems

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Data mining analysis to validate performance tuning practices for HPL

Data mining analysis to validate performance tuning practice...

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2009 IEEE International Conference on Cluster computing and Workshops, CLUSTER '09

作者： Tan, Tuan Zea Mong Goh, Rick Siow March, Verdi See, Simon Advanced Computing Institute of High Performance Computing Singapore Singapore Asia-Pacific Science and Technology Center Sun Microsystems Inc. Department of Computer Science National University of Singapore Singapore Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781424450121

Applications performance is a criterion for system evaluation, and hence performance tuning for these applications is of great interest. One such benchmark application is high performance Linpack (HPL). Although guidelines exist for HPL tuning, validating these guidelines on various systems is a challenging task as a large number of configurations need to be tested. In this work, we use data mining analysis to reduce the number of configurations to be tested in validating the HPL tuning guidelines on the Ranger System. We validate that NB, P and Q are the three most important parameters to tune HPL, and that PMAP does not have a significant impact on HPL performance. We also validate the practice of tuning HPL at small N using data mining analysis. We find that the value of N selected for tuning should not be significantly smaller than the largest N that can fit into the system memory. Our results indicate that data mining could be further applied to application performance tuning. © 2009 IEEE.

关键词： Data mining

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A framework of user-driven data analytics in the cloud for course management

A framework of user-driven data analytics in the cloud for c...

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18th International Conference on computers in Education: Enhancing and Sustaining New Knowledge Through the Use of Digital Technology in Education, ICCE 2010

作者： Zhang, Jie Tjhi, William Chandra Lee, Bu Sung Lee, Kee Khoon Vassileva, Julita Looi, Chee Kit School of Computer Engineering Nanyang Technological University Singapore A Star Institute of High Performance Computing Singapore Department of Computer Science University of Saskatchewan Canada Centre of Excellence for Learning Innovation National Institute of Education Singapore

ISBN: (纸本)9789834251253

In this paper, we describe our goal of an effective course management system for assisting course managers to make informed decisions about what materials should be most appropriate to be presented to students (learners) and what learning strategies or methods should be used for the students. The system is supported by our design of a novel framework for user-driven data analytics in the cloud. Different modules of the framework will be illustrated in detail in the context of course management.

关键词： Learning systems

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An efficient parallel algorithm for longest common subsequence problem on GPUs

An efficient parallel algorithm for longest common subsequen...

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作者： Yang, Jiaoyun Xu, Yun Shang, Yi Key Laboratory on High Performance Computing Anhui Province China College of Computer Science and Technology University of Science and Technology of China Heifei Anhui China Department of Computer Science University of Missouri Columbia MO United States

ISBN: (纸本)9789881701299

Sequence alignment is an important problem in computational biology and finding the longest common subsequence (LCS) of multiple biological sequences is an essential and effective technique in sequence alignment. A major computational approach for solving the LCS problem is dynamic programming. Several dynamic programming methods have been proposed to have reduced time and space complexity. As databases of biological sequences become larger, parallel algorithms become increasingly important to tackle large size problems. In the meantime, general-purpose computing on graphics processing units (GPGPU) has emerged as a promising technology for cost-effective high performance computing. In this paper, we develop an efficient parallel algorithm on GPUs for the LCS problem. We propose a new technique that changes the data dependency in the score table used by dynamic programming algorithms to enable higher degrees of parallelism. The algorithm takes advantage of the large number of processing units and the unique memory-accessing properties of GPUs to achieve high performance. The algorithm was implemented on Nvidia 9800GT GPUs and tested on randomly generated sequences of different lengths. The experiment results show that the new algorithm is about 6 times faster on GPUs than on typical CPUs and is 3 times faster than an existing efficient parallel algorithm, the diagonal parallel algorithm.

关键词： Parallel algorithms

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DISCRETE ENERGY ANALYSIS OF THE THIRD-ORDER VARIABLE-STEP BDF TIME-STEPPING FOR DIFFUSION EQUATIONS

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Journal of Computational Mathematics 2023年第2期41卷 325-344页

作者： Hong-lin Liao Tao Tang Tao Zhou Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing 211106China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

This is one of our series works on discrete energy analysis of the variable-step BDF *** this part,we present stability and convergence analysis of the third-order BDF(BDF3)schemes with variable steps for linear diffusion equations,see,e.g.,[SIAM ***.,58:2294-2314]and[***.,90:1207-1226]for our previous works on the BDF2 *** this aim,we first build up a discrete gradient structure of the variable-step BDF3 formula under the condition that the adjacent step ratios are less than 1.4877,by which we can establish a discrete energy dissipation ***-robust stability and convergence analysis in the L^(2) norm are then *** the mesh robustness means that the solution errors are well controlled by the maximum time-step size but independent of the adjacent time-step *** also present numerical tests to support our theoretical results.

关键词： Diffusion equations Variable-step third-order BDF scheme Discrete gradient structure Discrete orthogonal convolution kernels Stability and convergence

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Simulative performance analysis of gossip failure detection for scalable distributed systems

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Cluster computing 1999年第3期2卷 207-217页

作者： Burns, Mark W. George, Alan D. Wallace, Bradley A. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Three protocols for gossip-based failure detection services in large-scale heterogeneous clusters are analyzed and compared. The basic gossip protocol provides a means by which failures can be detected in large distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. The hierarchical protocol leverages the underlying network topology to achieve faster failure detection. In addition to studying the effectiveness and efficiency of these two agreement protocols, we propose a third protocol that extends the hierarchical approach by piggybacking gossip information on application-generated messages. The protocols are simulated and evaluated with a fault-injection model for scalable distributed systems comprised of clusters of workstations connected by high-performance networks, such as the CPlant system at Sandia National Laboratories. The model supports permanent and transient node and link failures, with rates specified at simulation time, for processors functioning in a fail-silent fashion. Through high-fidelity, CAD-based modeling and simulation, we demonstrate the strengths and weaknesses of each approach in terms of agreement time, number of gossips, and overall scalability.

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Experimental Analysis of a Gossip-Based Service for Scalable, Distributed Failure Detection and Consensus

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Cluster computing 2003年第3期6卷 237-251页

作者： Sistla, Krishnakanth George, Alan D. Todd, Robert W. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Gossip protocols and services provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. Extending the gossip protocol such that a system reaches consensus on detected faults can be performed via a flat structure, or it can be hierarchically distributed across cooperating layers of nodes. In this paper, the performance of gossip services employing flat and hierarchical schemes is analyzed on an experimental testbed in terms of consensus time, resource utilization and scalability. performance associated with a hierarchically arranged gossip scheme is analyzed with varying group sizes and is shown to scale well. Resource utilization of the gossip-style failure detection and consensus service is measured in terms of network bandwidth utilization and CPU utilization. Analytical models are developed for resource utilization and performance projections are made for large system sizes.

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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An energy-efficient virtual machine scheduler with I/O collective mechanism in resource virtualisation environments

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International Journal of Networking and Virtual Organisations 2013年第4期13卷 311-326页

作者： Xiao, Peng Liu, Dongbo Department of Computer Science High-Performance Computing Center Hunan Institute of Engineering Hunan Province China

Recently, resource virtualisation has been proven effective for deploying large-scale IT-infrastructures, such as grids and clouds. However, many studies also indicate that the system's energy-efficiency will be reduced when I/O virtualisation is involved. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduler with aiming at reducing the energy-efficiency losses caused by I/O virtualisation. The proposed VM scheduler is incorporated with an I/O collective mechanism, which separates I/O-intensive VMs from CPU-intensive ones during the runtime and schedules them in a batch manner, so as to reduce the context-switching costs when scheduling intensive mixed workloads. Extensive experiments are conducted on various platforms by using different benchmarks to investigate the performance of the proposed policy. The experimental results indicate that when the virtualisation platform is in presence of mixed workloads, the proposed scheduler outperforms many existing VM schedulers in term of energy-efficiency. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Virtual machine

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Gossip-Style Failure Detection and Distributed Consensus for Scalable Heterogeneous Clusters

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Cluster computing 2001年第3期4卷 197-209页

作者： Ranganathan, Sridharan George, Alan D. Todd, Robert W. Chidester, Matthew C. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Gossip protocols provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. However, in order to be effective with application recovery and reconfiguration, these protocols require mechanisms by which failures can be detected with system-wide consensus in a scalable fashion. This paper presents three new gossip-style protocols supported by a novel algorithm to achieve consensus in scalable, heterogeneous clusters. The round-robin protocol improves on basic randomized gossiping by distributing gossip messages in a deterministic order that optimizes bandwidth consumption. Redundant gossiping is completely eliminated in the binary round-robin protocol, and the round-robin with sequence check protocol is a useful extension that yields efficient detection times without the need for system-specific optimization. The distributed consensus algorithm works with these gossip protocols to achieve agreement among the operable nodes in the cluster on the state of the system featuring either a flat or a layered design. The various protocols are simulated and evaluated in terms of consensus time and scalability using a high-fidelity, fault-injection model for distributed systems comprised of clusters of workstations connected by high-performance networks.

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