parallel algorithms and cluster computing implementations algorithms and applications pdf

Parallel algorithms and cluster computing implementations algorithms and applications pdf

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Implementations, Algorithms and Applications

Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers , which communicate and coordinate their actions by passing messages to one another from any system. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock , and independent failure of components.

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Reducing communication in graph neural network training. Distributed many-to-many protein sequence alignment using sparse matrices. A distributed-memory algorithm for computing a heavy-weight perfect matching on bipartite graphs.

Terabase-scale metagenome coassembly with MetaHipMer. Scientific reports , Parallel algorithms for finding connected components using linear algebra. Journal of Parallel and Distributed Computing , A high-throughput solver for marginalized graph kernels on GPU. Optimizing high performance Markov clustering for pre-exascale architectures.

The parallelism motifs of genomic data analysis. Philosophical Transactions of the Royal Society A , , GPU accelerated partial order multiple sequence alignment for long reads self-correction. Performance optimization, modeling and analysis of sparse matrix-matrix products on multi-core and many-core processors. Parallel Computing , , RDMA vs.

RPC for implementing distributed data structures. BCL: A cross-platform distributed data structures library. IEEE, Graph coloring on the GPU. LACC: a linear-algebraic algorithm for finding connected components in distributed memory. Extreme scale de novo metagenome assembly.

IEEE Press, Best Paper Nominee. Integrated model, batch, and domain parallelism in training neural networks. Design principles for sparse matrix multiplication on the GPU. Distinguished Paper and Best Artifact Award. High-performance sparse matrix-matrix products on Intel KNL and multicore architectures. HipMCL: A high-performance parallel implementation of the Markov clustering algorithm for large-scale networks.

Communication-avoiding optimization methods for distributed massive-scale sparse inverse covariance estimation. A work-efficient parallel sparse matrix-sparse vector multiplication algorithm. The reverse Cuthill-McKee algorithm in distributed-memory. Performance characterization of de novo genome assembly on leading parallel systems. In Intl. Computing maximum cardinality matchings in parallel on bipartite graphs via tree-grafting. A high performance block eigensolver for nuclear configuration interaction calculations.

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Minimizing communication in all-pairs shortest paths. Distributed memory breadth-first search revisited: Enabling bottom-up search. Graph partitioning for scalable distributed graph computations. AMS, A flexible open-source toolbox for scalable complex graph analysis. Parallel sparse matrix-matrix multiplication and indexing: Implementation and experiments.

Gilbert, and Steve Reinhardt.

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Embed Size px x x x x BarthMichael GriebelDavid E. KeyesRisto M. NieminenDirk RooseTamar Schlick. This work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publicationor parts thereof is permitted only under the provisions of the German Copyright Law of September 9,, in its current version, and permission for use must always be obtained from Springer.

Implementations, Algorithms and Applications

Hansen, S. Parker, C. Rauber, R. Reilein, G.

Embed Size px x x x x BarthMichael GriebelDavid E. KeyesRisto M. NieminenDirk RooseTamar Schlick.

During the last 20 years the increase in computing power, the development of e? These abilities have allowed to treat problems with a complexity which had been out of reach for analytical approaches. While the increase in perf- mance of single processes has been immense the increase of massive parallel computing as well as the advent of clustercomputershas opened up the pos- bilities to study realistic systems. This book presents major advances in high performance computing as well as major advances due to high performance computing. The progress made during the last decade rests on the achie- ments in three distinct science areas.


  • Niobe M. 16.04.2021 at 04:16

    In computer science , a parallel algorithm , as opposed to a traditional serial algorithm , is an algorithm which can do multiple operations in a given time.

  • Favio O. 20.04.2021 at 04:56

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  • Г‰lodie G. 23.04.2021 at 11:36

    Implementations, Algorithms and Applications Digitally watermarked, DRM-free​; Included format: PDF; ebooks can be used on all reading devices implementation of these algorithms on massively parallel and cluster computers we present.


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