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AQUILA: Adaptive parallel Computation of Graph Connectivity Queries 20

AQUILA: Adaptive Parallel Computation of Graph Connectivity ...

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29th international Conference on High-Performance parallel and distributed Computing (HPDC)

作者： Ji, Yuede Huang, H. Howie George Washington Univ Graph Comp Lab Washington DC 20052 USA

ISBN: (纸本)9781450370523

Graph connectivity algorithms answer whether two nodes in a graph are connected under specific conditions, which are beneficial to a number of applications, such as pattern recognition and cybersecurity. Unfortunately, existing graph computing frameworks support only a small number of connectivity algorithms and achieve low computation parallelism. In this paper, we have designed an adaptive parallel computation framework, AQILA, that covers a wide range of different highly optimized graph connectivity algorithms. Given a graph, AQILA first transforms the query if it can be answered with partial computation. During the computation, AQILA is able to greatly reduce the workload by up to 98%. Furthermore, AQILA identifies the irregular tasks in the connectivity algorithms and applies different parallel strategies for different tasks. As a result, AQILA significantly outperforms existing systems such as Multistep, Galois, Ligra, GraphChi, X-Stream, DFS, and Boost, by average 13x, 53x, 264x, 364x, 1, 369x, 45x, and 255x, respectively.

关键词： graph connectivity parallel computation

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Silent data access protocol for NVRAM + RDMA distributed storage 34

Silent data access protocol for NVRAM + RDMA distributed sto...

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34th IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2020

作者： Liu, Qingyue Varman, Peter Rice University Houston United States

ISBN: (纸本)9781728174457

Recent advances in hardware technologies raise new opportunities for architecting storage systems to exploit emerging NVRAM memory devices, fast remote-memory RDMA networking, and large numbers of processor cores. In this paper, we present Telepathy, a novel data access protocol for a distributed key-value storage system constructed around NVRAM and RDMA. Telepathy supports replicated data storage for fault tolerance and guarantees strong consistency while supporting high-volume concurrent read/write access from multiple client nodes. Our read protocol can perform largely silent consistent reads from any of the replica nodes holding an object, while our write protocol exploits remote atomic and non-volatile buffers to silently perform write contention resolution. Our experiments on an RDMA-enabled cluster using YCSB benchmarks show that Telepathy can achieve microsecond-range reads and writes with small tail latencies, GB-range data access bandwidth with small CPU utilization, and data consistency guarantees. © 2020 IEEE.

关键词： Fault tolerance

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17th international symposium on Applied Reconfigurable Computing, ARC 2021

17th International Symposium on Applied Reconfigurable Compu...

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17th international symposium on Applied Reconfigurable Computing, ARC 2021

ISBN: (纸本)9783030790240

The proceedings contain 25 papers. The special focus in this conference is on Applied Reconfigurable Computing. The topics include: Multi-layered NoCs with Adaptive Routing for Mixed Criticality systems;preface;fast Approximation of the Top-k Items in Data Streams Using a Reconfigurable Accelerator;PDU Normalizer Engine for Heterogeneous In-Vehicle Networks in Automotive Gateways;StreamGrid - An AXI-Stream-Compliant Overlay Architecture;Increasing Side-Channel Resistance by Netlist Randomization and FPGA-Based Reconfiguration;moving Target and Implementation Diversity Based Countermeasures Against Side-Channel Attacks;clone-Resistant Secured Booting Based on Unknown Hashing Created in Self-Reconfigurable Platform;transparent Near-Memory Computing with a Reconfigurable Processor;a Dataflow Architecture for Real-Time Full-Search Block Motion Estimation;providing Tamper-Secure SoC Updates Through Reconfigurable Hardware;graviton: A Reconfigurable Memory-Compute Fabric for Data Intensive Applications;Dynamic Spatial Multiplexing on FPGAs with OpenCL;exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in distributed databases;Accelerating Convolutional Neural Networks in FPGA-based SoCs using a Soft-Core GPU;Evaluating the Design Space for Offloading 3D FFT Calculations to an FPGA for High-Performance Computing;FPGA Implementation of Custom Floating-Point Logarithm and Division;On the Suitability of Read only Memory for FPGA-Based CAM Emulation Using Partial Reconfiguration;Domain-Specific Modeling and Optimization for Graph Processing on FPGAs;Covid4HPC: A Fast and Accurate Solution for Covid Detection in the Cloud Using X-Rays;Evaluation of Different Manual Placement Strategies to Ensure Uniformity of the V-FPGA;Timing Optimization for Virtual FPGA Configurations;Hardware Based Loop Optimization for CGRA Architectures.

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Tiled algorithms for efficient task-parallel ℌ-matrix solvers 34

Tiled algorithms for efficient task-parallel ℌ-matrix solve...

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34th IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2020

作者： Carratala-Saez, Rocio Faverge, Mathieu Pichon, Gregoire Sylvand, Guillaume Quintana-Orti, Enrique S. Universitat Jaume i Castelló de la Plana Spain Univ. Bordeaux Bordeaux INP CNRS Inria Talence France Univ Lyon EnsL UCBL CNRS Inria LIP Lyon France Airbus Central RT / Inria Bordeaux France Universitat Politècnica de València València Spain

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174457

In this paper, we describe and evaluate an extension of the CHAMELEON library to operate with hierarchical matrices (H-Matrices) and hierarchical arithmetic (H-Arithmetic), producing efficient solvers for linear systems arising in Boundary Element Methods (BEM). Our approach builds upon an open-source H-Matrices library from Airbus, named HMAT-OSS, that collects sequential numerical kernels for both hierarchical and low-rank structures;the tiled algorithms and task-parallel decompositions available in CHAMELEON for the solution of linear systems;and the STARPU runtime system to orchestrate an efficient task-parallel (multi-threaded) execution on a multicore architecture. Using an application producing matrices with features close to real industrial applications, we present shared-memory results that demonstrate a fair level of performance, close to (and sometimes better than) the one offered by a pure H-Matrix approach, as proposed by Airbus HMAT proprietary (and non open-source) library. Hence, this combination CHAMELEON + HMAT-oss proposes the most efficient fully open-source software stack to solve dense compressible linear systems on shared memory architectures (distributed memory is under development). © 2020 IEEE.

关键词： Linear systems

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A High-Performance distributed Relational Database System for Scalable OLAP Processing 33

A High-Performance Distributed Relational Database System fo...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Chicago IL 60616 USA

ISBN: (纸本)9781728112466

The scalability of systems such as Hive and Spark SQL that are built on top of big data platforms have enabled query processing over very large data sets. However, the per-node performance of these systems is typically low compared to traditional relational databases. Conversely, Massively parallel Processing (MPP) databases do not scale as well as these systems. We present HRDBMS, a fully implemented distributed shared-nothing relational database developed with the goal of improving the scalability of OLAP queries. HRDBMS achieves high scalability through a principled combination of techniques from relational and big data systems with novel communication and work-distribution techniques. While we also support serializable transactions, the system has not been optimized for this use case. HRDBMS runs on a custom distributed and asynchronous execution engine that was built from the ground up to support highly parallelized operator implementations. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS's scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases like Greenplum.

关键词： SQL big data distributed query processing

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Fractal Intersections and Products via Algorithmic Dimension

Fractal Intersections and Products via Algorithmic Dimension

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作者： Lutz, Neil Department of Computer Science Iowa State University AmesIA50011 United States

Algorithmic fractal dimensions quantify the algorithmic information density of individual points and may be defined in terms of Kolmogorov complexity. This work uses these dimensions to bound the classical Hausdorff and packing dimensions of intersections and Cartesian products of fractals in Euclidean spaces. This approach shows that two prominent, fundamental results about the dimension of Borel or analytic sets also hold for arbitrary sets. © 2021 ACM.

关键词： parallel processing systems

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Towards HPC I/O Performance Prediction Through Large-scale Log Analysis 20

Towards HPC I/O Performance Prediction Through Large-scale L...

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29th international Conference on High-Performance parallel and distributed Computing (HPDC)

作者： Kim, Sunggon Sim, Alex Wu, Kesheng Byna, Suren Son, Yongseok Eom, Hyeonsang Seoul Natl Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Chung Ang Univ Seoul South Korea

ISBN: (纸本)9781450370523

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

关键词： High Performance Computing distributed File System I/O Performance Prediction Log Analysis

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parallel simulation of stochastic reward nets using theatre 21

Parallel simulation of stochastic reward nets using theatre

引用

proceedings of the 2021 IEEE/ACM 25th international symposium on distributed Simulation and Real Time Applications

作者： Franco Cicirelli Libero Nigro CNR - National Research Council of Italy Italy University of Calabria Italy

ISBN: (纸本)9781665433266

This work aims at the development of tools for supporting modelling and analysis of timed systems by Stochastic Reward Nets (SRN). In a first approach it was proposed and experimented a formal reduction of SRN over Timed Automata (TA) in the context of the Uppaal popular toolbox. The reduction has the merit to allow both exhaustive model checking of an SRN model, useful for the assessment of qualitative properties (e.g., absence of deadlocks, occurrence of particular event sequences etc.), and quantitative analysis through the statistical model checker, which is based on simulations. However, although Uppaal enabled formal reasoning on the semantics of SRN, its practical usage suffers of scalability problems, that is it can introduce severe limitations in time and space when studying complex models. To cope with this problem, this paper describes a Java implementation of the SRN operational core engine, using the lock-free and efficient Theatre actor system which permits the parallel simulation of large models. The realization can be used for functional property checking on an untimed version of a source SRN model, and quantitative estimation of measurables through simulations. The paper discusses the design and implementation of the core engine of SRN on top of Theatre, together with supported intuitive configuration process of an SRN model, and reports some experimental results using a scalable grid computing model. The experiments confirm Theatre/SRN are capable of exploiting the potential of modern multi-core machines and can deliver good execution performances on large models.

关键词： theatre Java stochastic reward nets actors high-performance computing performability analysis

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Simulating heterogeneous models on multi-core platforms using Julia's computing language parallel potential 20

Simulating heterogeneous models on multi-core platforms usin...

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proceedings of the IEEE/ACM 24th international symposium on distributed Simulation and Real Time Applications

作者： Sergey Suslov Michael Schiek Markus Robens Christian Grewing Stefan van Waasen ZEA-2 - Electronic Systems Germany

This paper addresses the questions of high-level system modelling using heterogeneous multi-tool modelling environment on parallel multi-core processing systems for simulation acceleration. The modelling technique has been applied for high-level validation of a high-precision Indoor Positioning System for Motion Analysis (IPS-MA) developed in the Central Institute Electronic systems (ZEA-2) of the Research Center Juelich GmbH. The heterogeneous modelling environment has been built using an implementation-level model designed in Matlab Simulink, a verification model for describing the system environment using Modelica language, and Julia language for automatic generation of binding modelling environment and parallelizing the simulation of the overall model. The approach showed a good flexibility in system description and verification in the multi-instrument modelling environment and a good performance gain due to simulation parallelism.

关键词： modelica Julia computing language high level system modelling parallelized simulation distributed simulation functional verification

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A distributed neural network architecture for robust non-linear spatio-temporal prediction 28

A distributed neural network architecture for robust non-lin...

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28th European symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Karlbauer, Matthias Otte, Sebastian Lensch, Hendrik P.A. Scholten, Thomas Wulfmeyer, Volker Butz, Martin V. University of Tübingen Neuro-Cognitive Modeling Group Sand 14 Tübingen72076 Germany University of Tübingen Computer Graphics Maria-von-Linden-Straße 6 Tübingen72076 Germany University of Tübingen - Soil Science and Geomorphology Rümelinstraße 19-23 Tübingen72070 Germany University of Hohenheim Institute for Physics and Meteorology Garbenstraße 30 Stuttgart70599 Germany

ISBN: (纸本)9782875870742

DISTANA - a distributed spatio-temporal artificial neural network architecture - learns to model and predict spatio-temporal time series dynamics. It learns in a parallel, spatially distributed manner while employing a mesh of recurrent, neural prediction kernels (PKs). Individual PKs predict the local data stream and exchange information laterally. DISTANA essentially assumes that generally applicable causes, which may be locally modified, generate the observed data. We show that DISTANA scales and generalizes to large problem spaces, can approximate complex dynamics, and is robust to overfitting, outperforming other competitive ANNs. © ESANN 2020 - proceedings, 28th European symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Network architecture

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