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distributed Virtual Diskless Checkpointing: A Highly Fault Tolerant Scheme for Virtualized Clusters

Distributed Virtual Diskless Checkpointing: A Highly Fault T...

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

作者： Ben Eckart Xubin He Chentao Wu Ferrol Aderholdt Fang Han Stephen Scott Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Computer Engineering Virginia Commonwealth University Richmond VA USA Department of Computer Science Tennessee Technological University Cookeville TN USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781467309745

Today's high-end computing systems are facing a crisis of high failure rates due to increased numbers of components. Recent studies have shown that traditional fault tolerant techniques incur overheads that more than double execution times on these highly parallel machines. Thus, future high-end computing must be able to provide adequate fault tolerance at an acceptable cost or the burdens of fault management will severely affect the viability of such systems. Cluster virtualization offers a potentially unique solution for fault management, but brings significant overhead, especially for I/O. In this paper, we propose a novel diskless check pointing technique on clusters of virtual machines. Our technique splits Virtual Machines into sets of orthogonal RAID systems and distributes parity evenly across the cluster, similar to a RAID-5 configuration, but using VM images as data elements. Our theoretical analysis shows that our technique significantly reduces the overhead associated with check pointing by removing the disk I/O bottleneck.

关键词： Checkpointing Fault tolerant systems Fault tolerance Program processors Hardware Virtual machining Libraries

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Scalable Multi-threaded Community Detection in Social Networks

Scalable Multi-threaded Community Detection in Social Networ...

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

作者： Jason Riedy David A. Bader Henning Meyerhenke College of Computing Georgia Institute of Technology Atlanta GA USA Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany

The volume of existing graph-structured data requires improved parallel tools and algorithms. Finding communities, smaller sub graphs densely connected within the sub graph than to the rest of the graph, plays a role both in developing new parallel algorithms as well as opening smaller portions of the data to current analysis tools. We improve performance of our parallel community detection algorithm by 20% on the massively multithreaded Cray XMT, evaluate its performance on the next-generation Cray XMT2, and extend its reach to Intel-based platforms with OpenMP. To our knowledge, not only is this the first massively parallel community detection algorithm but also the only such algorithm that achieves excellent performance and good parallel scalability across all these platforms. Our implementation analyzes a moderate sized graph with 105 million vertices and 3.3 billion edges in around 500 seconds on a four processor, 80-logical-core Intel-based system and 1100 seconds on a 64-processor Cray XMT2.

关键词： Communities Clustering algorithms Measurement Instruction sets Arrays parallel algorithms Image edge detection

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Self-Adaptive Heterogeneous Cluster with Wireless Network

Self-Adaptive Heterogeneous Cluster with Wireless Network

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

作者： Xinyu Niu Kuen Hung Tsoi Wayne Luk Department of Computing School of Engineering Imperial College London UK

The high performance computing (HPC) community has been exploring novel platforms to push performance forward. Field Programmable Logic Arrays (FPGAs) and Graphics Processing Units (GPUs) have been widely used as accelerators for computational intensive applications. Heterogeneous cluster is one of the promising platforms as it combines characteristics of multiple processing elements, to meet requirements of various applications. In this work, we build a self-adaptive framework for heterogeneous clusters, coupled with a customised wireless network. A runtime cluster model is implemented to predict throughput, power and thermal merits for heterogeneous clusters. Cluster configurations are scheduled to improve cluster power efficiency, as well as to reduce peak temperature of processing elements. Results show that, for monitoring operations upon heterogeneous clusters, the customised wireless network provides stable and scalable performance for negligible overhead. A high performance application is developed under the proposed framework. Experiments show that this approach can improve both power efficiency and energy efficiency of N-body simulation for more than 15 times, while reducing device peak temperature by up to 12° C.

关键词： Runtime Adaptation models Monitoring Throughput Wireless networks Power demand Temperature measurement

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An Effective Self-adaptive Load Balancing Algorithm for Peer-to-Peer Networks

An Effective Self-adaptive Load Balancing Algorithm for Peer...

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

作者： Naixue Xiong Kaihua Xu Lilong Chen Laurence T. Yang Yuhua Liu School of Information Technology Jiangxi University of Finance and Economics Nanchang China College of Physical Science and Technology Huazhong Normal University China Department of Computer Science Huazhong Normal University China Department of Computer Science Saint Francis Xavier University Antigonish Canada

ISBN: (纸本)9781467309745

The field of parallel and distributed computing has become increasingly significant as recent advances in electronic and integrated circuit technologies. Peer-to-Peer (P2P) cloud computing networks are the largest contributor of network traffic on the Internet. Measurement plays an important role in different P2P applications, we should enhance the measurement-based optimization of P2P networking and applications. In especial, to enhance the file sharing efficiency in P2P networks while reducing the inter-domain traffic, extensive schemes are proposed and file sharing is becoming seriously concerned. However, difference in ability, free-riding behavior and high churn have caused great unbalance on load degree between high speed network nodes. This paper presents a self-adaptive load balancing algorithm, where nodes create binary tree back-up node tables for their shared hot files automatically, and transfer extra query quest connection sent originally to heavy-load nodes and to back-up nodes. The experimental results reveal our algorithm can reduce load degree of heavy-load nodes and bring ideal balance between high speed network nodes, although under high churn, it also has balance effect and lower load degree of the whole network systems.

关键词： Peer to peer computing Algorithm design and analysis Load modeling Entropy Load management Economics Educational institutions

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PGAS for distributed Numerical Python Targeting Multi-core Clusters

PGAS for Distributed Numerical Python Targeting Multi-core C...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Mads Ruben Burgdorff Kristensen Yili Zheng Brian Vinter Niels Bohr Institute University of Copenhagen Denmark Lawrence Berkeley National Laboratory Berkeley CA USA

In this paper we propose a parallel programming model that combines two well-known execution models: Single Instruction, Multiple Data (SIMD) and Single Program, Multiple Data (SPMD). The combined model supports SIMD-style data parallelism in global address space and supports SPMD-style task parallelism in local address space. One of the most important features in the combined model is that data communication is expressed by global data assignments instead of message passing. We implement this combined programming model into Python, making parallel programming with Python both highly productive and performing on distributed memory multi-core systems. We base the SIMD data parallelism on DistNumPy, an auto-parallel zing version of the Numerical Python (NumPy) package that allows sequential NumPy programs to run on distributed memory architectures. We implement the SPMD task parallelism as an extension to DistNumPy that enables each process to have direct access to the local part of a shared array. To harvest the multi-core benefits in modern processors we exploit multi-threading in both SIMD and SPMD execution models. The multi-threading is completely transparent to the user -- it is implemented in the runtime with Open MP and by using multi-threaded libraries when available. We evaluate the implementation of the combined programming model with several scientific computing benchmarks using two representative multi-core distributed memory systems -- an Intel Nehalem cluster with Infini band interconnects and a Cray XE-6 supercomputer -- up to 1536 cores. The benchmarking results demonstrate scalable good performance.

关键词： Arrays Libraries Data models parallel processing Computational modeling parallel programming

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Avoiding Serialization Effects in Data / Dependency Aware Task parallel Algorithms for Spatial Decomposition

Avoiding Serialization Effects in Data / Dependency Aware Ta...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Christoph Niethammer Colin W. Glass José Gracia High performance computing center Stuttgart University of Stuttgart Germany

Spatial decomposition is a popular basis for parallelising code. Cast in the frame of task parallelism, calculations on a spatial domain can be treated as a task. If neighbouring domains interact and share results, access to the specific data needs to be synchronized to avoid race conditions. This is the case for a variety of applications, like most molecular dynamics and many computational fluid dynamics codes. Here we present an unexpected problem which can occur in dependency-driven task parallelization models like StarSs: the tasks accessing a specific spatial domain are treated as interdependent, as dependencies are detected automatically via memory addresses. Thus, the order in which tasks are generated will have a severe impact on the dependency tree. In the worst case, a complete serialization is reached and no two tasks can be calculated in parallel. We present the problem in detail based on an example from molecular dynamics, and introduce a theoretical framework to calculate the degree of serialization. Furthermore, we present strategies to avoid this unnecessary problem. We recommend treating these strategies as best practice when using dependency-driven task parallel programming models like StarSs on such scenarios.

关键词： Runtime parallel processing Computational modeling Cascading style sheets Memory management parallel programming

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Automated Workload Characterization in Cloud-based Transactional Data Grids

Automated Workload Characterization in Cloud-based Transacti...

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

作者： Bruno Ciciani Diego Didona Pierangelo Di Sanzo Roberto Palmieri Sebastiano Peluso Francesco Quaglia Paolo Romano Sapienza-University of Rome Rome Italy INESC-ID/IST Lisboa Portugal

ISBN: (纸本)9781467309745

Cloud computing represents a cost-effective paradigm to deploy a wide class of large-scale distributed applications, for which the pay-per-use model combined with automatic resource provisioning promise to reduce the cost of dependability and scalability. However, a key challenge to be addressed to materialize the advantages promised by Cloud computing is the design of effective auto-scaling and self-tuning mechanisms capable of ensuring pre-determined QoS levels at minimum cost in face of changing workload conditions. This is one of the keys goals that are being pursued by the Cloud-TM project, a recent EU project that is developing a novel, self-optimizing transactional data platform for the cloud. In this paper we present the key design choices underlying the development of Cloud-TM's Workload Analyzer (WA), a crucial component of the Cloud-TM platform that is change of three key functionalities: aggregating, filtering and correlating the streams of statistical data gathered from the various nodes of the Cloud-TM platform, building detailed workload profiles of applications deployed on the Cloud-TM platform, characterizing their present and future demands in terms of both logical (i.e. data) and physical (e.g. hardware-related) resources, triggering alerts in presence of violations (or risks of future violations) of pre-determined SLAs.

关键词： Monitoring distributed databases Quality of service Computational modeling Computer architecture Algorithm design and analysis Analytical models

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Skyline Query Processing on Interval Uncertain Data

Skyline Query Processing on Interval Uncertain Data

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IEEE international symposium on Object/Component/Service-Oriented Real-Time distributed computing Workshops (ISORCW)

作者： Xiaoyong Li Yijie Wang Xiaoling Li Guangdong Wang National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Many recent applications involve processing and analyzing uncertain data. Recently, several research efforts have addressed answering skyline queries efficiently on massive uncertain datasets. However, the research lacks methods to compute these queries on uncertain data, where each dimension of the uncertain object is represented as an interval or an exact value. In this paper, we extensively study the problem of skyline query on these interval based uncertain objects, which has never been studied before. We first model the problem of querying the skylines on interval datasets. Typically, we address two efficient algorithms with I/O optimal for the conventional interval skyline queries and constrained interval skyline queries, respectively. Extensive experiments demonstrate the efficiency of all our proposed algorithms.

关键词： Probability density function Distribution functions Probability distribution Random variables Computational modeling Uncertainty distributed databases

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Performance Benefits of Heterogeneous computing in HPC Workloads

Performance Benefits of Heterogeneous Computing in HPC Workl...

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

作者： Victor W. Lee Ed Grochowski Robert Geva Intel Corporation Santa Clara CA USA

Chip multi-processors (CMPs) with increasing number of processor cores are now becoming widely available. To take advantage of many-core CMPs, applications must be parallelized. However, due to the nature of algorithm/programming model, some parts of the application would remain serial. According to Amdahl's law, the speedup of a parallel application is limited by the amount of serial execution it has. For a CMP with many cores, this can be a serious limitation. To take full advantage of the increasing number of cores, one must try to reduce the execution time of the serial portion of a parallel program. However, rewriting an application takes time and often the return on the effort invested may not justify parallelizing every part of the program. Heterogeneous many-core CMP design is one possible solution to support massive parallel execution and to provide a reasonable single-thread performance. In this paper, we use a simple spreadsheet model to evaluate homogeneous and heterogeneous CMP designs using execution profiles of real HPC applications. Evaluated on 12 parallel HPC applications, we show that heterogeneous CMPs can outperform homogeneous CMPs by up to 1.35× with an average speedup of 1.06× when both the heterogeneous CMPs and homogeneous CMPs are constrained to use the same power budget. Our study found the heterogeneous CMPs can take advantage of serial portion of execution that is as little as 2% of total run time to provide performance benefit. This suggests heterogeneous computing can help mitigate the effect of not parallelizing some portions of an application due to return on investment concern on programming efforts.

关键词： Instruction sets Benchmark testing Throughput Transistors Computational modeling Servers Power demand

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Dynamic parallel computing model of the aircraft landing on atrocious sea condition

Dynamic parallel computing model of the aircraft landing on ...

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The Eleventh international symposium on distributed computing and Applications to Business,Engineering and Science(DCABES2012)(第十一届分布式计算及其应用国际学术研讨会)(原名:电子商务、工程及科学领域的分布式计算和应用国际学术研讨会)

作者： Wang Dan Shesheng Zhang Li Mengyu Association of Mathematical Modeling Wuhan University of TechnologyWuhan430070

In the atrocious sea conditions,the aircraft forced landing on water is a significant research project of the military affairs and *** the impact of waves on aircraft,the dynamic parallel computing model of aircraft forced landing is established,and the impact force's function of the angle between the aircraft and the slant wave surface is established,and the relationship between this angle and impact force at different wind speeds is *** results show that the aircraft's best forced landing status is: aircraft pitch angle parallel to the wave angle.

关键词： aircraft atrocious sea conditions dynamic model

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