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检索条件"任意字段=19th IASTED International Conference on Parallel and Distributed Computing and Systems"

共 560 条记录，以下是221-230 订阅

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Dynamic Agent Platform Discovery for Real-Time Resource Selection

Dynamic Agent Platform Discovery for Real-Time Resource Sele...

引用

IEEE international conference on Intelligent Engineering systems

作者： Z. Balogh E. Gatial L. Hluchy Institute of Informatics Slovak Academy of Sciences/Department of Parallel and Distributed Computing

ISBN: (纸本)9781479908271

In this paper we present a dynamic agent platform discovery system which is used for real-time resource selection during crisis situations. We present a motivating use case and we describe the implementation and design details of the proposed system. Comparison of invocation mechanisms of agents as a direct result of AP discovery is discussed. We also present a scenario on which the discovery mechanism was evaluated.

关键词： Agents systems comparison Platform CALLING(PROGRAMMING)

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Proceedings of the 19th iasted international conference on parallel and distributed computing and systems

Proceedings of the 19th IASTED International Conference on P...

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19th iasted international conference on parallel and distributed computing and systems

the proceedings contain 84 papers. the topics discussed include: impact of NUMA effects on high-speed networking with multi-opteron machines;an ensemble method using hybrid real-coded genetic algorithm with pruning (HRGA/P R);P2P video broadcast based on per-peer transcoding and its evaluation on planetlab;dynamic modification of wireless multihop transmission route in message-by-message manner for shorter transmission delay;an efficient partitioning and scheduling algorithm for streaming applications on FPGA with resource constraint;associativity-based adaptive weighted clustering for large-scale mobile ad hoc networks;directional node-disjoint multipath routing in wireless ad hoc network;toward more parallel frequent itemset mining algorithms;schedulability of aperiodic tasks in hybrid process model;GRID-enabled ensemble subsurface modeling;and parallel double divide and conquer and its evaluation on a super computer.

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A Load-sharing Architecture for High Performance Optimistic Simulations on Multi-core Machines

A Load-sharing Architecture for High Performance Optimistic ...

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19th international conference on High Performance computing (HiPC)

作者： Vitali, Roberto Pellegrini, Alessandro Quaglia, Francesco Univ Rome DIIAG Rome Italy

ISBN: (纸本)9781467323703;9781467323727

In parallel Discrete Event Simulation (PDES), the simulation model is partitioned into a set of distinct Logical Processes (LPs) which are allowed to concurrently execute simulation events. In this work we present an innovative approach to load-sharing on multi-core/multiprocessor machines, targeted at the optimistic PDES paradigm, where LPs are speculatively allowed to process simulation events with no preventive verification of causal consistency, and actual consistency violations (if any) are recovered via rollback techniques. In our approach, each simulation kernel instance, in charge of hosting and executing a specific set of LPs, runs a set of worker threads, which can be dynamically activated/deactivated on the basis of a distributed algorithm. the latter relies in turn on an analytical model that provides indications on how to reassign processor/core usage across the kernels in order to handle the simulation workload as efficiently as possible. We also present a real implementation of our load-sharing architecture within the ROme OpTimistic Simulator (ROOT-Sim), namely an open-source C-based simulation platform implemented according to the PDES paradigm and the optimistic synchronization approach. Experimental results for an assessment of the validity of our proposal are presented as well.

关键词： discrete event simulation distributed algorithms multiprocessing systems parallel architectures performance evaluation

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Towards Scalable Optimal Sequence Homology Detection

Towards Scalable Optimal Sequence Homology Detection

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19th international conference on High Performance computing (HiPC)

作者： Daily, Jeff Krishnamoorthy, Sriram Kalyanaraman, Ananth Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781467323703;9781467323727

the field of bioinformatics and computational biology is experiencing a data revolution - experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. this has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a fundamental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large-scale optimal homology detection on massively parallel super-computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 x 10(7) optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

关键词： Cray computers DNA bioinformatics distributed memory systems mainframes parallel machines pattern recognition

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Visualization of Network Data Provenance

Visualization of Network Data Provenance

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19th international conference on High Performance computing (HiPC)

作者： Chen, Peng Plale, Beth Cheah, You-Wei Ghoshal, Devarshi Jensen, Scott Luo, Yuan Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781467323703;9781467323727

Visualization facilitates the understanding of scientific data both through exploration and explanation of the visualized data. Provenance also contributes to the understanding of data by containing the contributing factors behind a result. the visualization of provenance, although supported in existing workflow management systems, generally focuses on small (medium) sized provenance data, lacking techniques to deal with big data with high complexity. this paper discusses visualization techniques developed for exploration and explanation of provenance, including layout algorithm, visual style, graph abstraction techniques, and graph matching algorithm, to deal with the high complexity. We demonstrate through application to two extensively analyzed case studies that involved provenance capture and use over three year projects, the first involving provenance of a satellite imagery ingest processing pipeline and the other of provenance in a large-scale computer network testbed.

关键词： data visualisation distributed processing

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Scalable Performance of ScaleGraph for Large Scale Graph Analysis

Scalable Performance of ScaleGraph for Large Scale Graph Ana...

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19th international conference on High Performance computing (HiPC)

作者： Dayarathna, Miyuru Houngkaew, Charuwat Ogata, Hidefumi Suzumura, Toyotaro Tokyo Inst Technol Dept Comp Sci Tokyo Japan IBM Res Tokyo Japan

ISBN: (纸本)9781467323703;9781467323727

Scalable analysis of massive graphs has become a challenging issue in high performance computing environments. ScaleGraph is an X10 library aimed for large scale graph analysis scenarios. this paper evaluates scalability of ScaleGraph library for degree distribution calculation, betweeness centrality, and spectral clustering algorithms. We make scalability evaluation by analyzing a synthetic Kronecker graph with 40.3 million edges (for all the three algorithms), and a real social network with 69 million edges (for degree distribution calculation) on Tsubame 2.0 distributed memory environment.

关键词： X10 PGAS HPCS large graph analytics reusable libraries programming techniques distributed computing

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Campaign Scheduling

Campaign Scheduling

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19th international conference on High Performance computing (HiPC)

作者： Pinheiro, Vinicius Rzadca, Krzysztof Trystram, Denis Univ Sao Paulo Lab Parallel & Distributed Comp BR-05508 Sao Paulo Brazil Univ Warsaw Inst Informat Warsaw Poland Inst Univ France Grenoble Inst Technol Paris France

ISBN: (纸本)9781467323703;9781467323727

We study the problem of scheduling in parallel systems with many users. We analyze scenarios with many submissions issued over time by several users. these submissions contain one or more jobs;the set of submissions are organized in successive campaigns. Jobs belonging to a single campaign are sequential and independent, but any job from a campaign cannot start until all the jobs from the previous campaign are completed. Each user's goal is to minimize the sum of flow times of his campaigns. We define a theoretical model for Campaign Scheduling and show that, in the general case, it is NP-hard. For the single-user case, we show that an rho-approximation scheduling algorithm for the (classic) parallel job scheduling problem is also an rho-approximation for the Campaign Scheduling problem. For the general case with k users, we establish a fairness criterion inspired by time sharing. We propose FAIRCAMP, a scheduling algorithm which uses campaign deadlines to achieve fairness among users between consecutive campaigns. We prove that FAIRCAMP increases the flow time of each user by a factor of at most k rho compared with a machine dedicated to the user. We also prove that FAIRCAMP is a rho-approximation algorithm for the maximum stretch. By simulation, we compare FAIRCAMP to the First-Come-First-Served (FCFS). We show that, compared with FCFS, FAIRCAMP reduces the maximum stretch by up to 3 : 4 times. the difference is significant in systems used by many (k > 5) users. Our results show that, rather than just individual, independent jobs, campaigns of jobs can be handled by the scheduler efficiently and fairly.

关键词： approximation theory computational complexity parallel processing scheduling

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Fault Tolerant parallel Data-Intensive Algorithms

Fault Tolerant Parallel Data-Intensive Algorithms

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19th international conference on High Performance computing (HiPC)

作者： Kutlu, Mucahid Agrawal, Gagan Kurt, Oguz Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Dept Math Columbus OH 43210 USA

ISBN: (纸本)9781467323703;9781467323727

Fault-tolerance is rapidly becoming a crucial issue in high-end and distributed computing, as increasing number of cores are decreasing the mean-time to failure of the systems. While checkpointing, including checkpointing of parallel programs like MPI applications, provides a general solution, the overhead of this approach is becoming increasingly unacceptable. thus, algorithm-based fault-tolerance provides a nice practical alternative, though it is less general. Although this approach has been studied for many applications, there is no existing work for algorithm-based fault-tolerance for the growing class of data-intensive parallel applications. In this paper, we present an algorithm-based fault tolerance solution that handles fail-stop failures for a class of data intensive algorithms. We divide the dataset into smaller data blocks and in replication step, we distribute the replicated blocks with the aim of keeping the maximum data intersection between any two processors minimum. this allows us to have minimum data loss when multiple failures occur. In addition, our approach enables better load balance after failure, and decreases the amount of re-processing of the lost data. We have evaluated our approach by using two popular parallel data mining algorithms, which are k-means and apriori. We show that our approach has negligible overhead when there are no failures, and allows us to gracefully handle different number of failures, and failures at different points of processing. We also provide the comparison of our approach with the MapReduce based solution for fault tolerance, and show that we outperform Hadoop both in absence and presence of failures.

关键词： checkpointing data mining message passing parallel processing software fault tolerance

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Efficient update of ghost regions using active messages

Efficient update of ghost regions using active messages

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19th international conference on High Performance computing (HiPC)

作者： Milthorpe, Josh Rendell, Alistair P. Australian Natl Univ Res Sch Comp Sci Canberra ACT Australia

ISBN: (纸本)9781467323703;9781467323727

the use of ghost regions is a common feature of many distributed grid applications. A ghost region holds local read-only copies of remotely-held boundary data which are exchanged and cached many times over the course of a computation. X10 is a modern parallel programming language intended to support productive development of distributed applications. X10 supports the "active message" paradigm, which combines data transfer and computation in one-sided communications. A central feature of X10 is the distributed array, which distributes array data across multiple places, providing standard read and write operations as well as powerful high-level operations. We used active messages to implement ghost region updates for X10 distributed arrays using two different update algorithms. Our implementation exploits multiple levels of parallelism and avoids global synchronization;it also supports split-phase ghost updates, which allows for overlapping computation and communication. We compare the performance of these algorithms on two platforms: an Intel x86-64 cluster over QDR InfiniBand, and a Blue Gene/P system, using both stand-alone benchmarks and an example computational chemistry application code. Our results suggest that on a dynamically threaded architecture, a ghost region update using only pairwise synchronization exhibits superior scaling to an update that uses global collective synchronization.

关键词： parallel programming models Partitioned Global Address Space (PGAS) X10 language active messages structured grids distributed arrays ghost regions lattice Boltzmann method Smooth Particle Mesh Ewald method

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A distributed Abnormal Packet Generation Engine Based on MapReduce

A Distributed Abnormal Packet Generation Engine Based on Map...

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19th international conference on High Performance computing (HiPC)

作者： Zhang Qi-fei Lv Hong-bin Pan Xue-zeng Wang Chao Li Wen-juan Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Hangzhou Normal Univ Coll Qianjiang Hangzhou 310036 Zhejiang Peoples R China

ISBN: (纸本)9781467323703;9781467323727

With the maturity of Internet and rapid development of the Mobile Internet, the network protocols drafted by IETF, 3GPP, 3GPP2 and other standard organizations grow massively, and at the same time attacks against the network protocols are also increasing rapidly. the Robustness test for the network protocols is becoming increasingly important, since the protocols and applications will be absolutely safe if the protocols have been tested according to the requirements of the Robustness Test. But Robustness Test is different from Conformance Test, Interoperability Test and Performance Test in its requirement of a huge number of test cases, and the number of test cases is more than 2(boolean AND)320 for a packet with 40-Byte protocol header, thus, it's too difficult to generate all the test cases for the serial algorithm. In this paper a parallel algorithm based on MapReduce is proposed, where an original input packet is divided into different fields and the Map function processes on each single field and the Reduce function reassembles the processed fields to a new completed packet. Finally, experiment results show the parallel algorithm is far better than the serial algorithm when generating large data set. In addition, the scalability of the cluster is also verified.

关键词： MapReduce parallel computing Robustness testing Abnormal Packet Hadoop

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