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Formalizing Data Locality in Task parallel Applications 16th

Formalizing Data Locality in Task Parallel Applications

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16th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ceballos, German Hagersten, Erik Black-Schaffer, David Uppsala Univ Dept Informat Technol Uppsala Sweden

ISBN: (纸本)9783319499567;9783319499550

Task-based programming provides programmers with an intuitive abstraction to express parallelism, and runtimes with the flexibility to adapt the schedule and load-balancing to the hardware. Although many profiling tools have been developed to understand these characteristics, the interplay between task scheduling and data reuse in the cache hierarchy has not been explored. these interactions are particularly intriguing due to the flexibility task-based runtimes have in scheduling tasks, which may allow them to improve cache behavior. this work presents StatTask, a novel statistical cache model that can predict cache behavior for arbitrary task schedules and cache sizes from a single execution, without programmer annotations. StatTask enables fast and accurate modeling of data locality in task-based applications for the first time. We demonstrate the potential of this new analysis to scheduling by examining applications from the BOTS benchmarks suite, and identifying several important opportunities for reuse-aware scheduling.

关键词： Task-based Cache modeling Performance model

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the Co-design of Astrophysical Code for Massively parallel Supercomputers 16th

The Co-design of Astrophysical Code for Massively Parallel S...

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16th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Glinsky, Boris Kulikov, Igor Chernykh, Igor Weins, Dmitry Snytnikov, Alexey Nenashev, Vladislav Andreev, Andrey Egunov, Vitaly Kharkov, Egor Inst Computat Math & Math Geophys SB RAS Lavrentjeva Ave 6 Novosibirsk 630090 Russia Novosibirsk State Tech Univ Novosibirsk 630073 Russia Volgograd State Tech Univ Volgograd 400005 Russia

ISBN: (纸本)9783319499567;9783319499550

the rapid growth of supercomputer technologies became a driver for the development of natural sciences. Most of the discoveries in astronomy, in physics of elementary particles, in the design of new materials in the DNA research are connected with numerical simulation and with supercomputers. Supercomputer simulation became an important tool for the processing of the great volume of the observation and experimental data accumulated by the mankind. Modern scientific challenges put the actuality of the works in computer systems and in the scientific software design to the highest level. the architecture of the future exascale systems is still being discussed. Nevertheless, it is necessary to develop the algorithms and software for such systems right now. It is necessary to develop software that is capable of using tens and hundreds of thousands of processors and of transmitting and storing of large volumes of data. In the present work the technology for the development of such algorithms and software is proposed. As an example of the use of the technology, the process of the software development is considered for some problems of astrophysics.

关键词： Exascale systems Co-design High performance computing Computational astrophysics Physics of plasmas

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Dense Symmetric Indefinite Factorization on GPU Accelerated architectures 11th

Dense Symmetric Indefinite Factorization on GPU Accelerated ...

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11th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Baboulin, Marc Dongarra, Jack Remy, Adrien Tomov, Stanimire Yamazaki, Ichitaro Univ Paris 11 Orsay France Inria Orsay France Univ Tennessee Knoxville TN USA

ISBN: (纸本)9783319321493;9783319321486

We study the performance of dense symmetric indefinite factorizations (Bunch-Kaufman and Aasen's algorithms) on multicore CPUs with a Graphics processing Unit (GPU). though such algorithms are needed in many scientific and engineering simulations, obtaining high performance of the factorization on the GPU is difficult because the pivoting that is required to ensure the numerical stability of the factorization leads to frequent synchronizations and irregular data accesses. As a result, until recently, there has not been any implementation of these algorithms on hybrid CPU/GPU architectures. To improve their performance on the hybrid architecture, we explore different techniques to reduce the expensive communication and synchronization between the CPU and GPU, or on the GPU. We also study the performance of an LDLT factorization with no pivoting combined with the preprocessing technique based on Random Butterfly Transformations. though such transformations only have probabilistic results on the numerical stability, they avoid the pivoting and obtain a great performance on the GPU.

关键词： Dense symmetric indefinite factorization Communication-avoiding Randomization GPU computation

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Implementation of the Beamformer Algorithm for the NVIDIA Jetson 16th

Implementation of the Beamformer Algorithm for the NVIDIA Je...

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16th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Alventosa, Fran J. Alonso, Pedro Pinero, Gema Vidal, Antonio M. Univ Politecn Valencia Dept Informat Syst & Computat DSIC Valencia Spain Univ Politecn Valencia Inst Telecomunicac & Aplicac Multimedia iTEAM Valencia Spain

ISBN: (纸本)9783319499567;9783319499550

Nowadays, the aim of the technology industry is intensively shifting to improve the ratio Gflop/watt of computation. Many processors implement the low power design of ARM architecture like, e.g. the NVIDIA TK1, a chip which also includes a GPU embedded in the same die to improve performance at a low energy consumption. this type of devices are very suitable target machines to be used on applications that require mobility like, e.g. those that manage and reproduce real acoustics environments. One of the most used algorithms in these reproduction environments is the Beamformer Algorithm. We have implemented the variant called Beamformer QR-LCMV, based on the QR decomposition, which is a very computationally demanding operation. We have explored different options differing basically in the high performance computing library used. Also we have built our own version with the aim of approaching the real-time processing goal when working on this type of low power devices.

关键词： Audio processing Beamformer GPU-CPU processing Heterogeneous QR Factorization

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Scaling DBSCAN-like algorithms for Event Detection Systems in Twitter 16th

Scaling DBSCAN-like Algorithms for Event Detection Systems i...

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16th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Capdevila, Joan Pericacho, Gonzalo Torres, Jordi Cerquides, Jesus Polytech Univ Catalonia UPC Dept Comp Architec Barcelona Spain Barcelona Supercomp Ctr BSC CNS Dept Comp Sci Barcelona Spain Artificial Intelligence Res Inst IIIA CSIC Barcelona Spain

ISBN: (纸本)9783319495835;9783319495828

the increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. the proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. the results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.

关键词： Event detection parallel algorithm Data clustering DBSCAN MapReduce Apache Spark Twitter

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Task Allocation for Distributed Stream processing 35

Task Allocation for Distributed Stream Processing

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35th IEEE Annual international conference on Computer Communications (IEEE INFOCOM)

作者： Eidenbenz, Raphael Locher, thomas ABB Corp Res Krakow Poland

ISBN: (纸本)9781467399531

there is a growing demand for live, on-the-fly processing of increasingly large amounts of data. In order to ensure the timely and reliable processing of streaming data, a variety of distributed stream processing architectures and platforms have been developed, which handle the fundamental tasks of (dynamically) assigning processing tasks to the currently available physical resources and routing streaming data between these resources. However, while there are plenty of platforms offering such functionality, the theory behind it is not well understood. In particular, it is unclear how to best allocate the processing tasks to the given resources. In this paper, we establish a theoretical foundation by formally defining a task allocation problem for distributed stream processing, which we prove to be NP-hard. Furthermore, we propose an approximation algorithm for the class of series-parallel decomposable graphs, which captures a broad range of common stream processing applications. the algorithm achieves a constant-factor approximation under the assumptions that the number of resources scales at least logarithmically with the number of computational tasks and the computational cost of the tasks dominates the cost of communication.

关键词： Approximation algorithms

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11th international conference on parallel processing and Applied Mathematics, PPAM 2015

11th International Conference on Parallel Processing and App...

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11th international conference on parallel processing and Applied Mathematics, PPAM 2015

ISBN: (纸本)9783319321486

the proceedings contain 56 papers. the special focus in this conference is on parallel architectures and Resilience, Numerical algorithms and parallel Scientific Computing. the topics include: Exploring memory error vulnerability for parallel programming models;an approach for ensuring reliable functioning of a supercomputer based on a formal model;sparse matrix multiplication on dataflow engines;energy efficient calculations of text similarity measure on FPGA-accelerated computing platforms;a bucket sort algorithm for the particle-in-cell method on manycore architectures;experience on vectorizing lattice boltzmann kernels for multi- and many-core architectures;performance analysis of the Kahan-enhanced scalar product on current multicore processors;performance analysis of the Chebyshev basis conjugate gradient method on the K computer;dense symmetric indefinite factorization on GPU accelerated architectures;a parallel multi-threaded solver for symmetric positive definite bordered-band linear systems;parallel algorithm for quasi-band matrix-matrix multiplication;comparative performance analysis of coarse solvers for algebraic multigrid on multicore and manycore architectures;LU preconditioning for overdetermined sparse least squares problems;experimental optimization of parallel 3D overlapping domain decomposition schemes;massively parallel approach to sensitivity analysis on HPC architectures by using scalarm platform;GPU implementation of Krylov solvers for block-tridiagonal eigenvalue problems;comparison of large graphs using distance information;fast incremental community detection on dynamic graphs;a diffusion process for graph partitioning;a parallel algorithm for LZW decompression, with GPU implementation;parallel FDFM approach for computing GCDs using the FPGA and parallel induction of nondeterministic finite automata.

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Scalability of parallel finite element algorithms on multi-core platforms 11

Scalability of parallel finite element algorithms on multi-c...

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11th international conference on Mesh Methods for Boundry-Value Problems and Applications

作者： Kopysov, S. P. Novikov, A. K. Nedozhogin, N. S. Rychkov, V. N. Russian Acad Sci Ural Branch Inst Mech 34 T Baramzinoy Izhevsk 426067 Russia

the speedup of element-by-element FEM algorithms depends not only on peak processor performance but also on access time to shared mesh data. Eliminating memory boundness would significantly speed up unstructured mesh computations on hybrid multi-core architectures, where the gap between processor and memory performance continues to grow. the speedup can be achieved by ordering unknowns so that only those elements are processed in parallel which do not have common nodes. therefore, memory conflicts are minimized. FEM assembly is performed with respect to the ordering, which defines how to compose vectors. Mesh can be partitioned into disjoint subdomains by using different layer-by-layer schemes. In this work, we evaluated several partitioning schemes (block, odd, even, and their modifications) on multi-core platforms, using Gunther's Universal Law of Computational Scalability. We performed numerical experiments with element-by-element matrix-vector multiplication on unstructured meshes on multi-core processors accelerated by MIC and GPU. With ordering, we achieved 5-times speedup on CPU, 40-times speedup on MIC, and 200-times speedup on GPU.

关键词： Graphics processing unit

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Scheduling Algorithm with Plotter in Cloud Environment 5

Scheduling Algorithm with Plotter in Cloud Environment

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5th international conference System Modeling and Advancement in Research Trends (SMART)

作者： Dixit, Richa Yadav, Vibhash Singhai, Rahul Dr AKT Univ Lucknow Uttar Pradesh India KIOT Kanpur Uttar Pradesh India Davi Ahilya Univ IIPS Indore MP India

ISBN: (纸本)9781509035434

Cloud computing is a rising technology. Scheduling techniques performs a major role in clouds because it process vast amount of data. We design a protocol to reduce the execution time and maximize the throughput and improve the performance of the server and also try to reduce the drawback of the existing protocols. We use the priority based load balancing algorithm based on the nature of the job to improve the performance of the system as well as minimize the response time. Here we emphasis on the existing job scheduling drawbacks and improvise the throughput. Finally we compare the proposed algorithm with various existing algorithm on different parameters.

关键词： Scheduling Plotter Cloud Computing Load Balancing Resource Allocation throughput Scheduling Protocol Priority Based Scheduling parallel Computing parallel Job Scheduling Batch processing Work Loads Optimized Schedule Minimized Make Span

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OpenMP Genetic Algorithm for Continuous Nonlinear Large-Scale Optimization Problems 5th

OpenMP Genetic Algorithm for Continuous Nonlinear Large-Scal...

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5th international conference on Soft Computing for Problem Solving (SocProS)

作者： Umbarkar, A. J. Walchand Coll Engn Dept Informat Technol Sangli MS India

ISBN: (纸本)9789811004513;9789811004506

Genetic algorithms (GAs) are one of the evolutionary algorithms for solving continuous nonlinear large-scale optimization problems. In an optimization problem, when dimension size increases, the size of search space increases exponentially. It is quite difficult to explore and exploit such huge search space. GA is highly parallelizable optimization algorithm;still there is a challenge to use all the cores of multicore (viz. Dual core, Quad core, and Octa cores) systems. the paper analyzes the parallel implementation of SGA (Simple GA) called as OpenMP GA. OpenMP (Open Multi-processing) GA attempts to explore and exploit the search space on the multiple cores' system. the performance of OpenMP GA is compared with SGA with respect to time required and cores utilized for obtaining optimal solution. the results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and CPU utilization. In case of OpenMP GA, CPU utilization is almost double for continuous nonlinear large-scale test problems for the given system configuration.

关键词： Function optimization Genetic algorithm (GA) Open multi-processing (OpenMP) Nonlinear optimization problems Optimization benchmarks functions

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