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The Large Scale Data Facility: Data Intensive computing for Scientific Experiments

The Large Scale Data Facility: Data Intensive Computing for ...

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

作者： Ariel O. Garcia Serguei Bourov Ahmad Hammad Jos van Wezel Bernhard Neumair Achim Streit Volker Hartmann Thomas Jejkal Patrick Neuberger Rainer Stotzka Steinbuch Centre of Computing Karlsruhe Institute of Technology Karlsruhe Germany Institute of Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany

The Large Scale Data Facility (LSDF) at the Karlsruhe Institute of Technology was started end of 2009 with the aim of supporting the growing requirements of data intensive experiments. In close cooperation with the involved scientific communities, the LSDF provides them not only with adequate storage space but with a directly attached analysis farm and - more importantly - with value added services for their big scientific data-sets. Analysis workflows are supported through the mixed Hadoop and Open Nebula Cloud environments directly attached to the storage, and enable the efficient processing of the experimental data. Metadata handling is a central part of the LSDF, where a metadata repository, community specific metadata schemes, graphical tools, and APIs were developed for accessing and efficiently organizing the stored data-sets.

关键词： Software Data visualization Communities Microscopy Throughput Protocols Memory

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Accelerating Protein Sequence Search in a Heterogeneous computing System

Accelerating Protein Sequence Search in a Heterogeneous Comp...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Shucai Xiao Heshan Lin Wu-chun Feng Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA

The "Basic Local Alignment Search Tool'' (BLAST) is arguably the most widely used computational tool in bioinformatics. However, the computational power required for routine BLAST analysis has been outstripping Moore's Law due to the exponential growth in the size of the genomic sequence databases that BLAST searches on. To address the above issue, we propose the design and optimization of the BLAST algorithm for searching protein sequences (i.e., BLASTP) in a heterogeneous computing system. The end result is a BLASTP implementation that delivers a seven-fold speedup over the sequential BLASTP for the most computationally intensive phase (i.e., hit detection and ungapped extension) on a NVIDIA Fermi C2050 GPU. In addition, when pipelining the processing on a dual-core CPU and the NVIDIA Fermi GPU, our implementation can achieve a six-fold speedup for the overall program execution.

关键词： Graphics processing unit Instruction sets Databases Kernel Proteins Computer architecture Acceleration

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From Simulation to Experiment: A Case Study on Multiprocessor Task Scheduling

From Simulation to Experiment: A Case Study on Multiprocesso...

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

作者： Sascha Hunold Henri Casanova Frederic Suter LIG Laboratory CNRS Grenoble France Department of Information and Computer Sciences University of Hawaii Mānoa USA IN2P3 Computing Center CNRS Lyon-Villeurbanne France

Simulation is a popular approach for empirically evaluating the performance of algorithms and applications in the parallel computing domain. Most published works present results without quantifying simulation error. In this work we investigate accuracy issues when simulating the execution of parallel applications. This is a broad question, and we focus on a relevant case study: the evaluation of scheduling algorithms for executing mixed-parallel applications on clusters. Most such scheduling algorithms have been evaluated in simulation only. We compare simulations to real-world experiments in a view to identify which features of a simulator are most critical for simulation accuracy. Our first finding is that simple yet popular analytical simulation models lead to simulation results that cannot be used for soundly comparing scheduling algorithms. We then show that, by contrast, simulation models instantiated based on brute-force measurements of the target execution environment lead to usable results. Finally, we develop empirical simulation models that provide a reasonable compromise between the two previous approaches.

关键词： Analytical models Program processors Computational modeling Scheduling algorithm Schedules Simulation Scheduling

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An Integrated Scratch Management Service for HPC Centers

An Integrated Scratch Management Service for HPC Centers

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

作者： Henry M. Monti Department of Computer Science Virginia Technology USA

To sustain emerging data-intensive scientific applications, High Performance computing (HPC) centers invest a notable fraction of their operating budget on a specialized fast storage system, scratch space, which is designed for storing the data of currently running and soon-to-run HPC jobs. Instead, it is often used as a standard file system, wherein users arbitrarily store their data, without any consideration to the center's overall performance. To remedy this, centers periodically scan the scratch in an attempt to purge transient and stale data. This practice of supporting a cache workload using a file system and disjoint tools for staging and purging results in sub optimal use of the scratch space. This work addresses the above issues by proposing a new perspective, where the HPC scratch space is treated as a cache, and data population, retention, and eviction tools are integrated with scratch management. Using this approach, data is moved to the scratch space only when it is needed, and unneeded data is removed as soon as possible.

关键词： Cloud computing Bandwidth Fuses Transient analysis Availability Proposals

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Towards a Self-Adaptive Data Management System for Cloud Environments

Towards a Self-Adaptive Data Management System for Cloud Env...

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

作者： Alexandra Carpen-Amarie INRIA/IRISA Rennes France

The emergence of Cloud computing brings forward many challenges that may limit the adoption rate of the Cloud paradigm. As data volumes processed by applications running on Clouds increase, the need for efficient and secure data management emerges as a crucial requirement. This work aims to enable BlobSeer, a large-scale data management system, as a Cloud data service, by addressing a series of self-management requirements. The first step towards an autonomic behavior was to equip the BlobSeer platform with introspection capabilities, which can serve as input data for a self-adaptive engine designed to address such goals as self-configuration, self-optimization or self-protection. We developed the self-protection direction within a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. In addition, we designed an expressive policy description language enabling system administrators to define a large array of security attacks and to enforce various types of restrictions upon the detected malicious clients. Finally, we are integrating our data-management system as a storage back end within the Nimbus Cloud environment.

关键词： Security Monitoring Cloud computing distributed databases Throughput Availability Clouds

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An Efficient Search Algorithm without Memory for Peer-to-Peer Cloud computing Networks

An Efficient Search Algorithm without Memory for Peer-to-Pee...

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

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

In recent years, as the rapid development of the technology about Peer-to-Peer (P2P) networks and the cloud computing technology, various applications of P2P technology become very widespread in most cloud computing distributed network applications. P2P cloud computing networks are unstructured and are an important component to implement next generation internet. How to quickly and efficiently search the resources in P2P networks has become one of the most critical issues, and it is one of the greatest concerns to users. This paper first describes the basic flooding P2P network search method, which is from the analyzing the pros and cons of several new search methods. After that, a cache-based search algorithm is proposed: When a node of the remaining load capacity is high, it will become the center node and form a joint topology area with the nearby nodes together, then the center node and ordinary nodes also need to store the index cache. At the local region the overheating resources will be copied to the local (that is, the contents cache). The simulation results show that the algorithm can effectively improve the hit rates of resources searching, and reduce the query delay in cloud computing networks.

关键词： Peer to peer computing Topology Joints Indexes Routing Network topology Algorithm design and analysis

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Improving Data Locality of MapReduce by Scheduling in Homogeneous computing Environments

Improving Data Locality of MapReduce by Scheduling in Homoge...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohong Zhang Zhiyong Zhong Shengzhong Feng Bibo Tu Jianping Fan Shenzhen Institutes of Advanced Technology Chinese Academy and Sciences Shenzhen China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China Chinese Academy and Sciences Beijing China Institute of Computing Technology Chinese Academy and Sciences Beijing China

Data Locality is one of the critical factors to affect performance. This paper proposes a next-k-node scheduling (NKS) method to improve the data locality of map tasks. The method first calculates the probabilities of each map task, and then preferentially schedules the one with the highest probability. It generates low probabilities for the tasks which satisfy node locality with the nodes to issue requests, so it can reserve these tasks to these nodes. We have implemented the NKS method in hadoop-0.20.2. The experiment results have shown that the NKS method reduced 78% of the map tasks processed without node locality, reduced 77%of the network load caused by the tasks, and improved the performance of Hadoop MapReduce when comparing with the default task scheduling method in Hadoop. Obviously, the NKS method is very suitable for the homogeneous environment with network overload.

关键词： Schedules Probability distributed databases Radio access networks Scheduling Topology Data models

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Self-Stabilizing Master-Slave Token Circulation and Efficient Topology Computation in a Tree of Arbitrary Size

Self-Stabilizing Master-Slave Token Circulation and Efficien...

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

作者： Wayne Goddard Pradip K. Srimani School of Computing Clemson University Clemson SC USA

Self-stabilizing algorithms represent an extension of distributed algorithms in which nodes of the network have neither coordination, synchronization, nor initialization. We consider the model introduced by Lee et al. where there is one designated master node and all other nodes are anonymous and as simple as possible. We provide here a master-slave algorithm that achieves token circulation in a tree, and using that, enables the master node to compute the topology of the tree. We show that the complexity of the algorithm is at most 2n rounds O(n 2 ) steps, and the space complexity at the slave nodes is almost constant in the sense that storage needed collectively at all n slave nodes is 2n bits.

关键词： Topology Algorithm design and analysis Law Computational modeling distributed algorithms Complexity theory

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distributed Results Checking for MapReduce in Volunteer computing

Distributed Results Checking for MapReduce in Volunteer Comp...

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

作者： Mircea Moca Gheorghe Cosmin Silaghi Gilles Fedak Babes.-Bolyai University of Cluj-Napoca Romania INRIA Université de Lyon France

MapReduce is a promising approach to support data-intensive applications on Volunteer computing Systems. Existent middleware like Bit Dew allows running MapReduce applications in a Desktop Grid environment. If the Desktop Grid is deployed in the Internet under the Volunteer computing paradigm, it harnesses untrustable, volatile and heterogeneous resources and the results produced by MapReduce applications can be subject of sabotage. However, the implementation of large-scale MapReduce presents significant challenges with respect to the state of the art in Desktop Grid. A key issue is the design of the result certification, an operation needed to verify that malicious volunteers do not tamper with the results of computations. Because the volume of data produced and processed is so large that cannot be sent back to the server, the result certification cannot be centralized as it is currently implemented in Desktop Grid systems. In this paper we present a distributed result checker based on the Majority Voting method. We evaluate the efficiency of our approach using a model for characterizing errors and sabotage in the MapReduce paradigm. With this model, we can compute the aggregated probability with which a MapReduce implementation produces an erroneous result. The challenge is to capture the aggregated probability for the entire system, composed from probabilities resulted from the two phases of computation: Map and Reduce. We provide a detailed analysis on the performance of the result verification method and also discuss the generated overhead of managing security. We also give guidelines about how the result verification phase should be configured, given a MapReduce application.

关键词： Computational modeling Protocols Internet Middleware Security Servers distributed databases

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Toward parallel Document Clustering

Toward Parallel Document Clustering

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

作者： Jace A. Mogill David J. Haglin Pacific Northwest National Laboratory Richland WA USA

A key challenge to automated clustering of documents in large text corpora is the high cost of comparing documents in a multi-million dimensional document space. The Anchors Hierarchy is a fast data structure and algorithm for localizing data based on a triangle inequality obeying distance metric, the algorithm strives to minimize the number of distance calculations needed to cluster the documents into "anchors'' around reference documents called "pivots''. We extend the original algorithm to increase the amount of available parallelism and consider two implementations: a complex data structure which affords efficient searching, and a simple data structure which requires repeated sorting. The sorting implementation is integrated with a text corpora "Bag of Words'' program and initial performance results of end-to-end document processing workflow are reported.

关键词： Clustering algorithms parallel algorithms Indexes Data structures Concurrent computing Synchronization Semantics

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