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Efficient algorithms for improving the performance of read operations in distributed file system 23

Efficient algorithms for improving the performance of read o...

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23rd High Performance computing Symposium, HPC 2015, Part of the 2015 Spring Simulation Multi-conference, SpringSim 2015

作者： Krishna, T. Lakshmi Siva Rama Ragunathan, T. Battula, Sudheer Kumar Hyderabad India Computer Science and Engineering ACE Engineering College Hyderabad India

ISBN: (纸本)9781510810600

distributed file systems (DFSs) are used in the modern cloud-based systems to store and process a large amount of data. File sharing semantics is used by the DFS for sharing the data in a consistent manner among the authorized users of the system. The limitation of popular session semantics is that read client processes cannot read the modifications done by a concurrent write client process on the same shared file in the same session. Linearizability semantics followed in the Blob-Seer DFS permits the read client processes to read the previous version of a binary large object (blob) while write operation is carried out on that blob concurrently. In this paper, we have proposed a new type of semantics and metric namely "Speculative Semantics" and "Currency" respectively. Speculative semantics permits the read client processes to read the modifications done by the concurrent write client process in the same session. Currency is the metric used for measuring the performance of the read algorithms. In this paper, we have proposed two new read algorithms based on speculative semantics. We have conducted experiments on Blobseer DFS to measure the performance of these read algorithms and the results obtained indicate that the proposed algorithms perform better than existing read algorithm of the BlobSeer DFS. Copyright © 2015 Society for Modeling & Simulation international (SCS).

关键词： Semantics

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Integrating Data-Intensive computing systems with Biological Data Analysis Frameworks

Integrating Data-Intensive Computing Systems with Biological...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Edvard Pedersen Inge Alexander Raknes Martin Ernstsen Lars Ailo Bongo Department of Computer Science and Center for Bioinformatics University of Troms⊘ Norway Norstruct University of Troms⊘ Norway Now at Kongsberg Satellite Services AS Troms⊘ Norway

Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.

关键词： Pipelines Data analysis Pipeline processing File systems Biological information theory

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A Formal Specification and Prototyping Language for Multi-core System Management

A Formal Specification and Prototyping Language for Multi-co...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： A. Iliasov A. Rafiev F. Xia R. Gensh A. Romanovsky A. Yakovlev School of Computing Science Newcastle University UK

We relate the experience of a defining a formal domain specific language (DSL) for the construction and reasoning about OS-level management logic of multi-core systems. The approach is based on a novel, iterative development principle where results of prototyping studies feed back into the next language revision. We illustrate the DSL with several examples of executable scripts.

关键词： Instruction sets DSL Heating Clocks Computational modeling Switches Time-frequency analysis

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I/O Optimization in the Checkpointing of OpenMP Parallel Applications

I/O Optimization in the Checkpointing of OpenMP Parallel App...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Nuria Losada María J. Martín Gabriel Rodríguez Patricia González Computer Architecture Group University of A Coruña Spain

Despite the increasing popularity of shared-memory systems, there is a lack of tools for providing fault tolerance support to shared-memory applications. Check pointing is one of the most popular fault tolerance techniques. However, check pointing cost in terms of computing time, network utilization or storage resources can be a limitation for its practical use. This work proposes different techniques for the optimization of the I/O cost in the check pointing of shared-memory parallel applications. The proposals are extensively evaluated using the OpenMP NAS Parallel Benchmarks. Results show a significant decrease of the check pointing overhead.

关键词： Checkpointing Instruction sets Optimization Runtime Instruments Fault tolerance Fault tolerant systems

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Lattice Boltzmann Simulations at Petascale on Multi-GPU systems with Asynchronous Data Transfer and Strictly Enforced Memory Read Alignment

Lattice Boltzmann Simulations at Petascale on Multi-GPU Syst...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Fredrik Robertsén Jan Westerholm Keijo Mattila Department of Information Technologies Åbo Åbo Akademi University Finland Department of Physics Jyväskylä University of Jyväskylä Finland

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently general-purpose graphics processing units have become accessible as high-performance computing resources at large-scale. We report on implementing a lattice Boltzmann solver for multi-GPU systems that achieves 0.69 PFLOPS performance on 16384 GPUs. In addition to optimizing the data layout on the GPUs and eliminating the halo sites, we make use of the possibility to overlap data transfer between the host CPU and the device GPU with computing on the GPU. We simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on a large scale simulation using realistic input data.

关键词： Graphics processing units Kernel Lattice Boltzmann methods Bandwidth Computational modeling Data transfer

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A load balancing parallel method for frequent pattern mining on Multi-core cluster 23

A load balancing parallel method for frequent pattern mining...

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23rd High Performance computing Symposium, HPC 2015, Part of the 2015 Spring Simulation Multi-conference, SpringSim 2015

作者： Vu, Lan Alaghband, Gita University of Colorado Denver United States

ISBN: (纸本)9781510810600

In this paper, we present a new parallel method named SDFEM that enables frequent pattern mining (FPM) on cluster with multiple multi-core compute nodes to provide high performance. SDFEM is distinguished from previous parallel FPM works due to incorporating three advanced features to provide high mining performance for large-scale data analytic applications. First, SDFEM combines both shared memory and distributed memory computational models to leverage benefits of shared memory within a node in cluster. Second, it employs a multi-strategy load balancing approach to address the most challenging issue of parallel FPM to balance the mining workload among all cores of the cluster. Finally, its self-adaptive mining solution with the capability of dynamically adjusting to the characteristics of the database to perform efficiently on different data types either sparse or dense. For performance evaluation, we implement SDFEM using a hybrid model of OpenMP and MPI in which OpenMP is for the shared memory model and MPI is for message passing. SDFEM has been tested on a cluster of multiple 12-core shared memory compute nodes. Our experimental results on real databases show that performance of SDFEM is up to 329.5% faster than the parallel FPM approach that uses only distributed memory model with message passing (i.e. using pure MPI). In addition, SDFEM can achieve up to 45.4 - 64.8 speedup on 120 cores (i.e. 10 compute nodes and 12 cores per node). © 2015 Society for Modeling & Simulation international (SCS).

关键词： Database systems

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pNets: An Expressive Model for Parameterised Networks of Processes

pNets: An Expressive Model for Parameterised Networks of Pro...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Ludovic Henrio Eric Madelaine Min Zhang CNRS Univ. Nice Sophia Antipolis INRIA Sophia Antipolis Méditérannée France Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai CN

This article studies Parameterised Networks of Automata (pNets) from a theoretical perspective. We illustrate the expressiveness of pNets by showing how to express a wide range of classical constructs of (value-passing) process calculi, but also how we can easily encode complex interaction patterns used in modern distributed systems. Our framework can model full systems, using (closed) hierarchies of pNets, we can also build (open) pNet systems expressing composition operators. Concerning more fundamental aspects, we define a strong bisimulation theory specifically for the pNet model, prove its properties, and illustrate it on some examples. One of the original aspects of the approach is to relate the compositional nature of pNets with the notion of bisimulation, this is exemplified by studying the properties of a flattening operator for pNets.

关键词： Synchronization Cost accounting Semantics Context Algebra Indexes Computational modeling

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A Flexible and Portable Large-Scale DGEMM Library for Linpack on Next-Generation Multi-GPU systems

A Flexible and Portable Large-Scale DGEMM Library for Linpac...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： David Rohr Volker Lindenstruth Frankfurt Institute for Advanced Studies Frankfurt am Main Germany

In recent years, high performance computing has benefitted greatly from special accelerator cards such as GPUs. Matrix multiplication performed by the BLAS function DGEMM is one of the prime examples where such accelerators excel. DGEMM is the computational hotspot of many tasks, among them the Linpack benchmark. Current GPUs achieve more than 1 TFLOPS real performance in this task. Being connected via PCI Express, one can easily install multiple GPUs in a single compute node. This enables the construction of multi-TFLOPS systems out of off-the-shelf components. At such high performance, it is often complicated to feed the GPUs with sufficient data to run at full performance. In this paper we first analyze the scalability of our DGEMM implementation for multiple fast GPUs. Then we suggest a new scheme optimized for this situation and we present an implementation.

关键词： Graphics processing units Kernel Bandwidth Engines Benchmark testing Memory management Next generation networking

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TFluxSCC: Exploiting Performance on Future Many-Core systems through Data-Flow

TFluxSCC: Exploiting Performance on Future Many-Core Systems...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Andreas Diavastos Giannos Stylianou Pedro Trancoso Department of Computer Science University of Cyprus Nicosia Cyprus

The current trend in processor design is to increase the number of cores as to achieve a desired performance. While having a large number of cores on a chip seems to be feasible in terms of the hardware, the development of the software that is able to exploit that parallelism is one of the biggest challenges. In this paper we propose a Data-Flow based system that can be used to exploit the parallelism in large-scale many-core processors in an effective and efficient way. Our proposed system - TFlux SCC - is an extension of the TFlux Data-Driven Multithreading (DDM), which evolved to exploit the parallelism of the 48-core Intel Single-chip Cloud computing (SCC) processor. With TFlux SCC we achieve scalable performance using a global address space without the need of cache-coherency support. Our scalability study shows that application's performance can scale, with speedup results reaching up to 48x for 48 cores. The findings of this work provide insight towards what a Data-Flow implementation requires and what not from a many-core architecture in order to scale the performance.

关键词： Message systems Hardware Multicore processing Parallel processing Programming Runtime Software

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A Performance Isolation Analysis of Disk-Intensive Workloads on Container-Based Clouds

A Performance Isolation Analysis of Disk-Intensive Workloads...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Miguel G. Xavier Israel C. De Oliveira Fabio D. Rossi Robson D. Dos Passos Kassiano J. Matteussi Cesar A.F. De Rose Pontifical Catholic University of Rio Grande do SuI (PUCRS) Faculty of Informatics Porto Alegre Brazil Fac. of Inf. Pontificia Universidade Catolica do Rio Grande do Sul Porto Alegre RS BR Pontificia Universidade Catolica do Rio Grande do Sul Porto Alegre RS BR

The popularity of Cloud computing due to the increasing number of customers has led Cloud providers to adopt resource-sharing solutions to meet growing demand for infrastructure resources. As the adoption of resource-sharing/consolidation in Cloud computing became arguably a well-established solution, the ability the underlying virtualization systems of preventing performance interferences from customers must also be understood. Virtualization systems based on containers, such as LXC, are the basis of the next-generation of Cloud computing and have become the most popular solution under PaaS/IaaS Cloud platforms with the rise of Docker -- an open platform for developers and sysadmins to build, ship, and run distributed applications. Such platforms have enticed many attentions globally, since they leverage container-based virtualization systems to offer high scalability while low performance overheads, the performance might be solely aggravated if the customers' workloads are consolidated onto the same hardware and the isolation layer does not properly isolate the shared resources. Performance isolation is an inherent concern of such systems due to the nature as they are conceived and is still an unexplored and open research topic, the consequences might influence in the adoption under shared Cloud computing platforms where Quality-of-Service is a crucial factor that cannot be disregarded. In this paper we analyze the performance interference suffered by disk-intensive workloads within very noisy-perturbed containers (different hardware components stressed). Our results show workload combinations whose performance degradation goes up to 38%, but in contrast we expose a workload-balanced scenario wherein the performance does not suffer any interference.

关键词： Containers Virtualization Measurement Stress Hardware Cloud computing Benchmark testing

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