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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11321 条记录，以下是3791-3800 订阅

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Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing techniques

Evaluating Online Global Recovery with Fenix Using Applicati...

引用

international conference on parallel processing Workshops (ICPPW)

作者： Marc Gamell Daniel S. Katz Keita Teranishi Michael A. Heroux Rob F. Van der Wijngaart Timothy G. Mattson Manish Parashar Rutgers Discovery Informatics Institute Rutgers University Piscataway NJ USA NCSA ECE and GSLIS University of Illinois Urbana-Champaign IL USA Scalable Modeling and Analysis Department Sandia National Laboratories Livermore CA USA Scalable Algorithms Sandia National Laboratories Albuquerque NM USA Intel Corp Santa Clara CA US Intel Labs Intel Corp Santa Clara CA USA

ISBN: (纸本)9781509028269

Exascale systems promise the potential for computation at unprecedented scales and resolutions, but achieving exascale by the end of this decade presents significant challenges. A key challenge is due to the very large number of cores and components and the resulting mean time between failures (MTBF) in the order of hours or minutes. Since the typical run times of target scientific applications are longer than this MTBF, fault tolerance techniques will be essential. An important class of failures that must be addressed is process or node failures. While checkpoint/restart (C/R) is currently the most widely accepted technique for addressing processor failures, coordinated, stable-storage-based global C/R might be unfeasible at exascale when the time to checkpoint exceeds the expected MTBF. This paper explores transparent recovery via implicitly coordinated, diskless, application-driven checkpointing as a way to tolerate process failures in MPI applications at exascale. The discussed approach leverages User Level Failure Mitigation (ULFM), which is being proposed as an MPI extension to allow applications to create policies for tolerating process failures. Specifically, this paper demonstrates how different implementations of application-driven in-memory checkpoint storage and recovery compare in terms of performance and scalability. We also experimentally evaluate the effectiveness and scalability of the Fenix online global recovery framework on a production system -the Titan Cray XK7 at ORNL- and demonstrate the ability of Fenix to tolerate dynamically injected failures using the execution of four benchmarks and mini-applications with different behaviors.

关键词： Checkpointing Fault tolerant systems Redundancy Runtime Resilience

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An efficient parallel graph clustering technique using Pregel

An efficient parallel graph clustering technique using Prege...

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international conference on Big Data and Smart Computing (BIGCOMP)

作者： Seok-Joo Lee Sungmin Kang Hongyeon Kim Jun-Ki Min School of Computer Science and Engineering KOREATECH CheonAn Republic of Korea

To represent complex structure, graph data is widely used in diverse applications. Among various techniques to extract meaningful information in graph data, graph clustering is an important task for the discovery of an underlying graph structure. However, the volume of graph data becomes large and increases fast recently as well as traditional clustering algorithms become computationally expensive as the size of data to be clustered increases. In this paper, we propose an efficient graph clustering algorithm running on Pregel which is one of prominent parallel processing models for large-scale graph data. Graph clustering technique merges the nodes into clusters such that the nodes in a cluster are strongly connected each other. To seek strongly connected nodes efficiently, we utilize Min-Hash which calculates similarity between vertices and/or clusters. In our experimental study, we demonstrate the efficiency and scalability of our parallel algorithm compared to existing algorithms.

关键词： Clustering algorithms Algorithm design and analysis Silicon Data mining Computational modeling Data models parallel processing

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MatrixMap: Programming Abstraction and Implementation of Matrix Computation for Big Data applications 21

MatrixMap: Programming Abstraction and Implementation of Mat...

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21st IEEE international conference on parallel and distributed Systems ICPADS

作者： Huangfu, Yaguang Cao, Jiannong Lu, Hongliang Liang, Guanqing Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China

ISBN: (纸本)9780769557854

The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel system for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed data structure called bulk key matrix and a computation interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with optimized data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.

关键词： Big Data parallel Programming Matrix Computation Machine Learning Graph processing

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parallel and distributed processing techniques and applications

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2015年

作者： Arabnia, Hamid R. Joe, Kazuki C. S. R. E. A.

Proceedings of the 2019 international conference on parallel and distributed processing techniques and applications (PDPTA'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada.

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Stela: Enabling Stream processing Systems to Scale-in and Scale-out On-demand

Stela: Enabling Stream Processing Systems to Scale-in and Sc...

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IEEE international conference on Cloud Engineering (IC2E)

作者： Le Xu Boyang Peng Indranil Gupta Department of Computer Science University of Illinois Yahoo! Inc.

The era of big data has led to the emergence of new real-time distributed stream processing engines like Apache Storm. We present Stela (STream processing ELAsticity), a stream processing system that supports scale-out and scale-in operations in an on-demand manner, i.e., when the user requests such a scaling operation. Stela meets two goals: 1) it optimizes post-scaling throughput, and 2) it minimizes interruption to the ongoing computation while the scaling operation is being carried out. We have integrated Stela into Apache Storm. We present experimental results using micro-benchmark Storm applications, as well as production applications from industry (Yahoo! Inc. and IBM). Our experiments show that compared to Apache Storm's default scheduler, Stela's scale-out operation achieves throughput that is 21-120% higher, and interruption time that is significantly smaller. Stela's scale-in operation chooses the right set of servers to remove and achieves 2X-5X higher throughput than Storm's default strategy.

关键词： Throughput Storms Measurement Servers Elasticity parallel processing Engines

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A High-performance DSM Leveraging Software Defined Network 44

A High-performance DSM Leveraging Software Defined Network

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44th Annual international conference on parallel processing Workshops (ICPPW)

作者： Gao Qiang Tong Weiqin Kausar, Samina Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9781467375894

distributed shared memory(DSM) is an implementation of the shared memory model in distributed systems. Software-defined networking (SDN), can be effectively exploited to improve the performance of DSM. In this research, we propose an SDN-based distributed shared memory architecture. This architecture improves distributed applications by tuning network transmission and offloading functionality to the underlying network. We present a dynamic SDN multicast mechanism to reduce the number of consistency-related messages without interfering the unrelated processes in the same multicast group. In addition to these, we design a state-aware transmission mechanism that reduces the contention delays by using multicast communication. Furthermore, a novel write-update mechanism based on SDN orderly multicast is presented. The experiments on the prototype system show that the distributed applications using SDN-based DSM yield remarkable performance improvements. Moreover, this type of prototype system is feasible to implement on existing hardware.

关键词： DSM big data in-memory processing SDN state-aware ordered reliable multicast

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More Effective Synchronization Scheme in ML using Stale Parameters

More Effective Synchronization Scheme in ML using Stale Para...

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IEEE international conference on High Performance Computing and Communications

作者： Yabin Li Han Wan Bo Jiang Xiang Long Sch. of Comput. Sci. & Eng. Beihang Univ. Beijing China

ISBN: (纸本)9781509042982

In Machine learning (ML) the model we use is increasingly important, and the model's parameters, the key point of the ML, are adjusted through iteratively processing a training dataset until convergence. Although data-parallel ML systems often engage a perfect error tolerance when synchronizing the model parameters for maximizing parallelism, the synchronization of model parameters may delay in completion, a problem that generally gets worse at a large scale. This paper presents a Bounded Asynchronous parallel (BAP) model of computation that allows computations using stale model parameters in order to reduce synchronization overheads. In the meanwhile, our BAP model ensures theoretical convergence guarantees for large scale data-parallel ML applications. This model permits distributed workers to use the stale parameters storing in the local cache, instead of waiting until the Parameter Server (PS) produces a new version. This expressively reduces the time workers spend on waiting. Furthermore, the BAP model guarantees the convergence of ML algorithm by bounding the maximum distance of the stale parameters. Experiments conducted on 4 cluster nodes with up to 32 GPUs showed that our model significantly improved the proportion of computing time relative to the waiting time and led to 1.2-2 X speedup. Besides, we elaborated how to choose the staleness threshold when considering the tradeoff between Efficiency and Speed.

关键词： tradeoff distributed systems Bounded Asynchronous parallel Bulk Synchronous parallel Total Asynchronous parallel stale parameters

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Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs 23

Generalized Extraction of Real-Time Parameters for Homogeneo...

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23rd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Ali, Hazem Ismail Akesson, Benny Pinho, Luis Miguel Polytech Inst Porto CISTER Res Ctr INESC TEC Oporto Portugal Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781479984909

Many embedded multi-core systems incorporate both dataflow applications with timing constraints and traditional real-time applications. Applying real-time scheduling techniques on such systems provides real-time guarantees that all running applications will execute safely without violating their deadlines. However, to apply traditional real-time scheduling techniques on such mixed systems, a unified model to represent both types of applications running on the system is required. Several earlier works have addressed this problem and solutions have been proposed that address acyclic graphs, implicit-deadline models or are able to extract timing parameters considering specific scheduling algorithms. In this paper, we present an algorithm for extracting real-time parameters (offsets, deadlines and periods) that are independent of the schedulability analysis, other applications running in the system, and the specific platform. The proposed algorithm: 1) enables applying traditional real-time schedulers and analysis techniques on cyclic or acyclic Homogeneous Synchronous Dataflow (HSDF) applications with periodic sources, 2) captures overlapping iterations, which is a main characteristic of the execution of dataflow applications, 3) provides a method to assign offsets and individual deadlines for HSDF actors, and 4) is compatible with widely used deadline assignment techniques, such as NORM and PURE. The paper proves the correctness of the proposed algorithm through formal proofs and examples.

关键词： Multi- and Many-Core Systems Real-Time Systems Homogeneous Synchronous Dataflow (HSDF) Hard Real-Time Streaming Dataflow applications Algorithms Cyclic Graphs

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Keynote 2

Keynote 2

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IEEE international conference on Cluster Computing

作者： David Abramson University of Queensland Brisbane QLD AU

ISBN: (纸本)9781509036547

Summary form only given. The rise of big data science has created new demands for modern computer systems. While floating point performance has driven computer architecture and system design for the past few decades, there is renewed interest in the speed at which data can be ingested and processed. Early exemplars such as Gordon, the NSF funded system at the San Diego Supercomputing Centre, shifted the focus from pure floating point performance to memory and IO rates. At the University of Queensland we have continued this trend with the design of FlashLite, a parallel cluster equipped with large amounts of main memory, flash disk, and a distributed shared memory system (ScaleMP's vSMP). This allows applications to place data “close” to the processor, enhancing processing speeds. Further, we have built a geographically distributed multi-tier hierarchical data fabric called MeDiCI, which provides an abstraction of very large data stores across the metropolitan area. MeDiCI leverages industry solutions such as IBM's Spectrum Scale and SGI's DMF platforms.

关键词： distributed shared memory systems cache storage distributed shared memory systems cache storage Computer systems FLOATING POINTS Computer Architecture Data science

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MicroRNA-Target Interaction: A parallel Approach for Computing Pairing Energy

MicroRNA-Target Interaction: A Parallel Approach for Computi...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Elisabetta Ronchieri Daniele D'Agostino Luciano Milanesi Ivan Merelli CNAF Section - Italian Institute for Nuclear Physics Bologna Italy Inst. for Appl. Math. & Inf. Technol. “E. Magenes” Genoa Italy Institute for Biomedical Technologies Segrate (Mi) Italy Institute for Biomedical Technologies Segrate Italy

MicroRNAs (or miRNA) are key regulators of gene expression, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Since miRNA are involved in the onset of many different diseases, the study of their interaction with the genome is very important to study. Although the experimental identification of miRNA can be performed using sequencing techniques, the characterization of their target is quite complex. Therefore, computational methods are required to focus the analysis on small set of possibilities. Unfortunately, the limited knowledge about the processes that regulate miRNA target association impairs the development of optimal algorithm for prediction of the regulated genes. This is the reason why a variety of miRNA target prediction algorithms are available, but results of their application are often inconsistent. Therefore, many miRNA targets have been computationally predicted, but only a limited number of these were experimentally validated. Different approaches can be used to recognize miRNA targets. These efforts have focused primarily on the quality of the sequence match between microRNA and target rather than on the role of the mRNA secondary structure in which the target is embedded. Nonetheless, it is known that the miRNA secondary structures contribute to target recognition, because there is an energetic cost to freeing base-pairing interactions within mRNA in order to make the target accessible for microRNA binding. This second approach can provide good results even when little is know about the conservation of the miRNA, but it has the drawback of being computationally very expensive. In this paper we propose a parallelization of PITA, which is one of the most popular algorithm that exploits energetic considerations for computing the miRNA-target predictions.

关键词： Prediction algorithms RNA Bioinformatics Genomics Target recognition Diseases Gene expression

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