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X-Ray Computed Tomography Applied to Objects of Cultural Heritage: Porting and Testing the Filtered Back-Projection Reconstruction Algorithm on Low Power Systems-on-Chip

X-Ray Computed Tomography Applied to Objects of Cultural Her...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Elena Corni Lucia Morganti Maria Pia Morigi Rosa Brancaccio Matteo Bettuzzi Giuseppe Levi Eva Peccenini Daniele Cesini Andrea Ferraro Department of Physics and Astronomy University of Bologna Bologna Italy Bologna Section Italian Institute for Nuclear Physics (INFN) Bologna Italy Enrico Fermi Center for Study and Research Roma Italy CNAF Section Italian Institute for Nuclear Physics (INFN) Bologna Italy

The embedded and high-performance computing (HPC) sectors, that in the past were completely separated, are now somehow converging under the pressure of two driving forces: the release of less power consuming server processors and the increased performance of the new low power Systems-on-Chip (SoCs) developed to meet the requirements of the demanding mobile market. This convergence allows the porting to low power embedded architectures of applications that were originally confined to traditional HPC systems. In this paper, we present our experience of porting the Filtered Back-projection Algorithm to a low power, low cost system-on-chip, the NVIDIA Tegra K1, which is based on a quad core ARM CPU and on a NVIDIA Kepler GPU. This Filtered Back-projection Algorithm is heavily used in 3D Tomography reconstruction software. The porting has been done exploiting various programming languages (i.e. OpenMP, CUDA) and multiple versions of the application have been developed to exploit both the SoC CPU and GPU. The performances have been measured in terms of 2D slices (of a 3D volume) reconstructed per time unit and per energy unit. The results obtained with all the developed versions are reported and compared with those obtained on a typical x86 HPC node accelerated with a recent NVIDIA GPU. The best performances are achieved combining the OpenMP version and the CUDA version of the algorithm. In particular, we discovered that only three Jetson TK1 boards, equipped with Giga Ethernet interconnections, allow to reconstruct as many images per time unit as a traditional server, using one order of magnitude less energy. The results of this work can be applied for instance to the construction of an energy-efficient computing system of a portable tomographic apparatus.

关键词： Graphics processing units Image reconstruction Computer architecture Filtering algorithms Computed tomography Radiography

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PDTL: parallel and distributed Triangle Listing for Massive Graphs 44

PDTL: Parallel and Distributed Triangle Listing for Massive ...

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

作者： Giechaskiel, Ilias Panagopoulos, George Yoneki, Eiko Univ Cambridge Cambridge England Univ Oxford Oxford England

ISBN: (纸本)9781467375887

This paper presents the first distributed triangle listing algorithm with provable CPU, I/O, Memory, and Network bounds. Finding all triangles (3-cliques) in a graph has numerous applications for density and connectivity metrics, but the majority of existing algorithms for massive graphs are sequential, while distributed versions of algorithms do not guarantee their CPU, I/O, Memory, or Network requirements. Our parallel and distributed Triangle Listing (PDTL) framework focuses on efficient external-memory access in distributed environments instead of fitting subgraphs into memory. It works by performing efficient orientation and load-balancing steps, and replicating graphs across machines by using an extended version of Hu et al.'s Massive Graph Triangulation algorithm. PDTL suits a variety of computational environments, from single-core machines to high-end clusters, and computes the exact triangle count on graphs of over 6B edges and 1B vertices (e.g. Yahoo graphs), outperforming and using fewer resources than the state-of-the-art systems PowerGraph, OPT, and PATRIC by 2x to 4x. Our approach thus highlights the importance of I/O in a distributed environment.

关键词： Triangle Listing Triangle Counting Big Data Massive Graphs I/O-Efficient Algorithm distributed Algorithm parallel Algorithm

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A meta-graph approach to analyze subgraph-centric distributed programming models

A meta-graph approach to analyze subgraph-centric distribute...

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IEEE international conference on Big Data

作者： Ravikant Dindokar Neel Choudhury Yogesh Simmhan Department of Computational and Data Sciences Indian Institute of Science Bangalore India

ISBN: (纸本)9781467390064

Component-centric distributed graph processing models that use bulk synchronous parallel (BSP) execution have grown popular. These overcome short-comings of Big Data platforms like Hadoop for processing large graphs. However, literature on formal analysis of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms is lacking. Here, we propose an coarse-grained analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models. We apply this sketch to subgraph- and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. We explore the impact of various graph partitioning techniques on the meta-graph, and the impact of the meta-graph on graph algorithms. This decouples large unwieldy graphs and their partitioning artifacts from their algorithmic analysis. We evaluate our approach for five spatial and powerlaw graphs, four different partitioning strategies, and for PageRank and Breadth First Search algorithms. We show that this novel analytical technique is simple, scalable and yet gives a reliable estimate of the number of supersteps, and the communication and computational complexities of the algorithms for various graphs.

关键词： Partitioning algorithms Algorithm design and analysis Analytical models Computational modeling Programming Data models Big data

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High performance computing on SpiNNaker neuromorphic platform: A case study for energy efficient image processing

High performance computing on SpiNNaker neuromorphic platfor...

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IEEE international conference on Performance, Computing and Communications (IPCCC)

作者： Indar Sugiarto Gengting Liu Simon Davidson Luis A. Plana Steve B. Furber School of Computer Science University of Manchester Manchester United Kingdom

ISBN: (纸本)9781509052530

This paper presents an efficient strategy to implement parallel and distributed computing for image processing on a neuromorphic platform. We use SpiNNaker, a many-core neuromorphic platform inspired by neural connectivity in the brain, to achieve fast response and low power consumption. Our proposed method is based on fault-tolerant finegrained parallelism that uses SpiNNaker resources optimally for process pipelining and decoupling. We demonstrate that our method can achieve a performance of up to 49.7 MP/J for Sobel edge detector, and can process 1600 × 1200 pixel images at 697 fps. Using simulated Canny edge detector, our method can achieve a performance of up to 21.4 MP/J. Moreover, the framework can be extended further by using larger SpiNNaker machines. This will be very useful for applications such as energy-aware and time-critical-mission robotics as well as very high resolution computer vision systems.

关键词： Image processing Neuromorphics Protocols Supercomputers Multicore processing

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A review of sentiment analysis in Twitter data using Hadoop

International Journal of Database Theory and Application

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international Journal of Database Theory and Application 2016年第1期9卷 77-86页

作者： Jaba Sheela, L. Department of Master of Computer Applications Panimalar Engineering College Chennai India

Twitter is an online social networking site which contains rich amount of data that can be a structured, semi-structured and un-structured data. In this work, a method which performs classification of tweet sentiment in Twitter is discussed. To improve its scalability and efficiency, it is proposed to implement the work on Hadoop Ecosystem, a widely-adopted distributed processing platform using the Map Reduce parallel processing paradigm. Finally, extensive experiments will be conducted on real-world data sets, with an expectation to achieve comparable or greater accuracy than the proposed techniques in literature. © 2016 SERSC.

关键词： Sentiment analysis

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Custody: Towards Data-Aware Resource Sharing in Cloud-Based Big Data processing

Custody: Towards Data-Aware Resource Sharing in Cloud-Based ...

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IEEE international conference on Cluster Computing

作者： Shiyao Ma Jingjie Jiang Bo Li Baochun Li Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Electrical and Computer Engineering University of Toronto

ISBN: (纸本)9781509036547

With the advent of big data processing frameworks, the performance of data-parallel applications is heavily affected by the time it takes to read input data, making it important to improve data locality. Existing methods in achieving data locality have primarily focused on selecting machines to place tasks of applications. Nevertheless, the set of machines that an application can choose from is determined by a cluster manager, which is oblivious to the location of data in existing resource sharing frameworks. In this paper, we design, implement and evaluate Custody, a new cluster management framework that helps to maximize data locality by allocating the executor processes with local access to data to those applications in need. Custody achieves this objective by dynamically collecting runtime information of an application's input data and by effectively allocating executors among and within applications through theoretic analyses of the data-aware resource sharing problem. With significantly better data locality, Custody avoids unnecessary network transfers and thus expedites job completion times. Our experimental results on a 100-node cluster demonstrate that Custody can improve the data locality for input tasks by 36.9% in comparison with Spark's default cluster manager. Meanwhile, it reduces the job completion times by 14.9% due to fewer network transfers.

关键词： Resource management Sparks distributed databases Big data Dynamic scheduling Runtime

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Solutions for processing K Nearest Neighbor Joins for Massive Data on MapReduce 23

Solutions for Processing K Nearest Neighbor Joins for Massiv...

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

作者： Song, Ge Rochas, Justine Huet, Fabrice Magoules, Frederic Univ Nice Sophia Antipolis CNRS I3S UMR 7271 F-06900 Sophia Antipolis France Ecole Cent Paris Chatenay Malabry France

ISBN: (纸本)9781479984909

Given a point p and a set of points S, the kNN operation finds the k closest points to p in S. It is a computational intensive task with a large range of applications such as knowledge discovery or data mining. However, as the volume and the dimension of data increase, only distributed approaches can perform such costly operation in a reasonable time. Recent works have focused on implementing efficient solutions using the MapReduce programming model because it is suitable for large scale data processing. Also, it can easily be executed in a distributed environment. Although these works provide different solutions to the same problem, each one has particular constraints and properties. There is no readily available comparison to help users choose the one most appropriate for their needs. This is the problem we address in this work. Firstly, we show that all kNN implementations go through a common workflow, which we use as a basis for classification. Secondly, we describe precisely the different techniques published so far. And lastly, we provide a set of objective criteria that can be used to make informed decisions.

关键词： kNN Join Data Partition Hadoop MapReduce

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DPX10: An Efficient X10 Framework for Dynamic Programming applications 44

DPX10: An Efficient X10 Framework for Dynamic Programming Ap...

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

作者： Wang, Chen Yu, Ce Sun, Jizhou Meng, Xiangfei Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Nankai Univ Coll Comp & Control Engn Natl Supercomp Ctr Tianjin Tianjin Peoples R China

ISBN: (纸本)9781467375887

X10 language and Asynchronous Partitioned Global Address Space (APGAS) model is an emerging mechanism for programming high-performance computers and commodity clusters. However, little work exists on distributed programming framework for dynamic programming (DP) problems based on X10 and APGAS model. In this paper we present DPX10, an efficient distributed X10 framework for DP applications. DPX10 enables developers to write highly efficient DP programs without much effort. A DPX10 program is specified by a directed acyclic graph (DAG) pattern and a compute method for the vertices. DPX10 provides eight commonly used DAG patterns and a simple API to create custom patterns. The system handles all the tiresome work of implementing parallelization including DAG distribution, vertices scheduling, and vertices communication. Moreover, a new recovery method for distributed arrays is developed to provide transparent fault tolerance. We describe the design of the framework and use four DP applications with up to a billion vertices on 120 cores to demonstrate its simplicity, efficiency, and scalability.

关键词： X10 APGAS dynamic programming programming framework

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A high speed implementation counter mode cryptography using hardware parallelism

A high speed implementation counter mode cryptography using ...

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conference on Information and Knowledge Technology

作者： Samad Najjar-Ghabel Shamim Yousefi Mina Zolfy Lighvan Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781509043361

Nowadays, cryptography is one of the common security mechanisms. Cryptography algorithms are used to make secure data transmission over unsecured networks. Vital applications are required to techniques that encrypt/decrypt big data at the appropriate time, because the data should be encrypted/decrypted are variable size and usually the size of them is large. In this paper, for the mentioned requirements, the counter mode cryptography (CTR) algorithm with Data Encryption Standard (DES) core is paralleled by using Graphics processing Unit (GPU). A secondary part of our work, this parallel CTR algorithm is applied on special network on chip (NoC) architecture that designed by Heracles toolkit. The results of numerical comparison show that GPU-based implementation can be achieved better runtime in comparison to the CPU-based one. Furthermore, our final implementations show that parallel CTR mode cryptography is achieved better runtime by using special NoC that applied on FPGA board in comparison to GPU-based and CPU ones.

关键词： Graphics processing units Encryption Software algorithms Algorithm design and analysis Field programmable gate arrays

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Ray-tracing domain decomposition methods for real-time simulation on multi-core and multi-processor systems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第16期28卷 4352-4364页

作者： Magoules, Frederic Gbikpi-Benissan, Guillaume Callet, Patrick Technol Res Inst SystemX 8 Ave Vauve F-91120 Palaiseau France CentraleSupelec F-92295 Chatenay Malabry France

This paper describes the use of domain decomposition methods for accelerating wave physics simulation. Numerical wave-based methods provide more accurate simulation than geometrical methods, but at a higher computation cost as well. In the context of virtual reality, the quality of the results is estimated according to human perception, what makes geometrical methods an interesting approach for achieving real-time physically-based rendering. Here, we investigate a geometrical method based on both beams and rays tracing, which we enhance by two levels of parallel processing. techniques from domain decomposition methods are coupled with classical parallel computing on both shared and distributed memory. Both optic and acoustic renderings are experimented to evaluate the acceleration impact of the domain decomposition scheme. Speedup measurements clearly show the efficiency of using domain decomposition methods for real-time simulation of wave physics. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： ray-tracing beam-tracing parallel computing domain decomposition method wave propagation acoustic optic

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