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Proceedings of the 22nd international symposium on High-performance parallel and distributed computing

作者： Paolo Costa Austin Donnelly Greg O'Shea Antony Rowstron Microsoft Research Cambridge United Kingdom

ISBN: (纸本)9781450319102

Cluster fabric interconnects that use 3D torus topologies are increasingly being deployed in data center clusters. In our prior work, we demonstrated that by using these topologies and letting applications implement custom routing protocols and perform operations on path, it is possible to increase performance and simplify development. However, these benefits cannot be achieved using mainstream point-to-point networking stacks such as TCP/IP or MPI, which hide the underlying topology and do not allow the implementation of any in-network *** this paper we describe CamCubeOS, a novel key-based communication stack, purposely designed from scratch for 3D torus fabric interconnects. We note that many of the applications used in clusters are key-based. Therefore, we designed CamCubeOS to natively support key-based operations. We select a virtual topology that perfectly matches the underlying physical topology and we use the keyspace to expose the physical locality, thus avoiding the typical overhead incurred by overlay-based *** report on our experience in building several applications on top of CamCubeOS and we evaluate their performance and feasibility using a prototype and large-scale simulations.

关键词： in-network processing key-based routing data center clusters 3D torus topologies

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2Q*: an effective, low-overhead, improved replacement algorithm for mail service applications in storage system

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2013年第1期36卷 63-73页

作者： Si, Chengxiang Meng, Xiaoxuan Xu, Lu Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

Any one algorithm cannot be effective for all applications;therefore, choosing one appropriate for one special application is very meaningful. In this article, we analyzed access patterns for mail service application, and acquired an algorithm appropriate for it. Moreover, based on analysis of performance characteristics for the current algorithm, we proposed an improved algorithm, called 2Q*, which behaves better than others for a full range of sizes. We also verify the effectiveness of 2Q* in a real buffer system, and join it with the main stream pre-fetch policy. The experimental results show that 2Q* can improve IO performance of mail service application effectively.

关键词： cache replacement algorithms mail service

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Performance Model for parallel Matrix Multiplication with Dryad: Dataflow Graph Runtime

Performance Model for Parallel Matrix Multiplication with Dr...

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2nd international Conference on Cloud and Green Computing / 2nd international Conference on Social Computing and its applications (CGC/SCA)

作者： Li, Hui Fox, Geoffrey Qiu, Judy Indiana Univ Bloomington Sch Informat & Comp Pervas Technol Inst Bloomington IN USA

ISBN: (纸本)9780769548647;9781467330275

In order to meet the big data challenge of today's society, several parallel execution models on distributed memory architectures have been proposed: MapReduce, Iterative MapReduce, graph processing, and dataflow graph processing. Dryad is a distributed data-parallel execution engine that model program as dataflow graphs. In this paper, we evaluated the runtime and communication overhead of Dryad in realistic settings. We proposed a performance model for Dryad implementation of parallel matrix multiplication (PMM) and extend the model to MPI implementations. We conducted experimental analyses in order to verify the correctness of our analytic model on a Windows cluster with up to 400 cores, Azure with up to 100 instances, and Linux cluster with up to 100 nodes. The final results show that our analytic model produces accurate predictions within 5% of the measured results. We proved some cases that using average communication overhead to model performance of parallel matrix multiplication jobs on common HPC clusters is the practical approach.

关键词： Dryad Dataflow Graph Performance Modeling MPI Matrix Multiplication

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distributed and parallel Subarray Beamforming for Underwater Real-Time 3-D Acoustic Imaging

Distributed and Parallel Subarray Beamforming for Underwater...

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2nd international Conference on Computer Science and Network Technology (ICCSNT)

作者： Han, Yeqiang Zhou, Fan Tian, Xiang Chen, Yaowu Zhejiang Univ Inst Adv Digital Technol & Instrumentat Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781467329644;9781467329637

In this paper, we propose a distributed and parallel subarray beamforming (DPS) algorithm for performing underwater real-time 3-D acoustic imaging. The principle idea of this algorithm is that the full populated sensor array is subdivided into two stage distributed subarrays and the beamforming is carried out in parallel for each stage subarray. The proposed algorithm overcomes the problems of massive computational load affecting the engineering applications of the conventional beamforming techniques, while achieving the similar resolutions. The memory requirement obtained with the proposed algorithm is reduced by four orders of magnitude and the computational requirement is reduced by two orders of magnitude when compared with the conventional beamforming (CBF). The simulation and the comparisons illustrate the results and the efficiency of the algorithm.

关键词： Underwater 3-D acoustic imaging sonar sensor array system sonar signal processing distributed and parallel subarray beamforming conventional beamforming

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Scalable in situ scientific data encoding for analytical query processing 13

Scalable in situ scientific data encoding for analytical que...

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Proceedings of the 22nd international symposium on High-performance parallel and distributed computing

作者： Sriram Lakshminarasimhan David A. Boyuka Saurabh V. Pendse Xiaocheng Zou John Jenkins Venkatram Vishwanath Michael E. Papka Nagiza F. Samatova North Carolina State University Oak Ridge National Laboratory Raleigh NC USA Argonne National Laboratory Argonne IL USA Argonne National Laboratory and Northern Illinois University Argonne IL USA

ISBN: (纸本)9781450319102

The process of scientific data analysis in high-performance computing environments has been evolving along with the advancement of computing capabilities. With the onset of exascale computing, the increasing gap between compute performance and I/O bandwidth has rendered the traditional method of post-simulation processing a tedious process. Despite the challenges due to increased data production, there exists an opportunity to benefit from "cheap" computing power to perform query-driven exploration and visualization during simulation time. To accelerate such analyses, applications traditionally augment raw data with large indexes, post-simulation, which are then repeatedly utilized for data exploration. However, the generation of current state-of-the-art indexes involve a compute- and memory-intensive processing, thus rendering them inapplicable in an in situ context. In this paper we propose DIRAQ, a parallel in situ, in network data encoding and reorganization technique that enables the transformation of simulation output into a query-efficient form, with negligible runtime overhead to the simulation run. DIRAQ begins with an effective core-local, precision-based encoding approach, which incorporates an embedded compressed index that is 3 -- 6x smaller than current state-of-the-art indexing schemes. DIRAQ then applies an in network index merging strategy, enabling the creation of aggregated indexes ideally suited for spatial-context querying that speed up query responses by up to 10x versus alternative techniques. We also employ a novel aggregation strategy that is topology-, data-, and memory-aware, resulting in efficient I/O and yielding overall end-to-end encoding and I/O time that is less than that required to write the raw data with MPI collective I/O.

关键词： compression query processing indexing exascale computing

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Boosting Memory Performance of Many-Core FPGA Device through Dynamic Precedence Graph

Boosting Memory Performance of Many-Core FPGA Device through...

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Annual IEEE symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Yu Bai Abigail Fuentes Michael Riera Mohammed Alawad Mingjie Lin Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

Emerging FPGA device, integrated with abundant RAM blocks and high-performance processor cores, offers an unprecedented opportunity to effectively implement single-chip distributed logic-memory (DLM) architectures [1]. Being “memory-centric”, the DLM architecture can significantly improve the overall performance and energy efficiency of many memory-intensive embedded applications, especially those that exhibit irregular array data access patterns at algorithmic level. However, implementing DLM architecture poses unique challenges to an FPGA designer in terms of 1) organizing and partitioning diverse on-chip memory resources, and 2) orchestrating effective data transmission between on-chip and off-chip memory. In this paper, we offer our solutions to both of these challenges. Specifically, 1) we propose a stochastic memory partitioning scheme based on the well-known simulated annealing algorithm. It obtains memory partitioning solutions that promote parallelized memory accesses by exploring large solution space; 2) we augment the proposed DLM architecture with a reconfigure hardware graph that can dynamically compute precedence relationship between memory partitions, thus effectively exploiting algorithmic level memory parallelism on a per-application basis. We evaluate the effectiveness of our approach (A3) against two other DLM architecture synthesizing methods: an algorithmic-centric reconfigurable computing architectures with a single monolithic memory (A1) and the heterogeneous distributed architectures synthesized according to [1] (A2). To make our comparison fair, in all three architectures, the data path remains the same while local memory architecture differs. For each of ten benchmark applications from SPEC2006 and MiBench [2], we break down the performance benefit of using A3 into two parts: the portion due to stochastic local memory partitioning and the portion due to the dynamic graph-based memory arbitration. All experiments have been conducted with a V

关键词： Random access memory Field programmable gate arrays Computer architecture Heuristic algorithms Hardware Partitioning algorithms parallel processing

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MapReduce Across distributed Clusters for Data-intensive applications

MapReduce Across Distributed Clusters for Data-intensive App...

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26th IEEE international parallel and distributed processing symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Wang, Lizhe Tao, Jie Marten, Holger Streit, Achim Khan, Samee U. Kolodziej, Joanna Chen, Dan Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China Steinbuch Ctr Comp Karlsruhe Inst Technol Karlsruhe Germany North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA Univ Bielsko Biala Dept Math & Comp Sci Bielsko Biala Poland China Univ Geosci Sch Comp Beijing Peoples R China

ISBN: (纸本)9780769546766

Recently, the computational requirements for largescale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.

关键词： MapReduce Hadoop Data Intensive Computing

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Hierarchical Codes in Bandwidth-Limited distributed Storage Systems

Hierarchical Codes in Bandwidth-Limited Distributed Storage ...

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2nd international Congress on Computer applications and Computational Science (CACS 2011)

作者： Huang, Zhen Liu, Lixia Peng, Yuxing Xue, Shoufu National Laboratary of Parallel and Distributed Processing National University of Defense Technology Changsha China Air Defence Command College Changsha China

ISBN: (纸本)9783642283079;9783642283086

distributed storage systems store data on the "unreliable" network peers that can leave the system at any moment and their network bandwidth is limited. In this case, the only way to assure reliability of the data is to add redundancy using either replication or erasure codes. As a generalization of replication, erasure codes require less storage space with the same reliability as replication. Recently, a near-optimal erasure code named Hierarchical Codes, has been proposed that can significantly reduce the repair traffic by reducing the number of nodes participating in repair, which is referred to as repair degree d. To overcome the complexity of reintegration and efficiently control the reliability of Hierarchical Codes, we refine two concepts called location and relocation, then we propose an integrated maintenance scheme, which allow us to tune the code construction.

关键词： Repair

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Taming massive distributed datasets: data sampling using bitmap indices 13

Taming massive distributed datasets: data sampling using bit...

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Proceedings of the 22nd international symposium on High-performance parallel and distributed computing

作者： Yu Su Gagan Agrawal Jonathan Woodring Kary Myers Joanne Wendelberger James Ahrens The Ohio State University Columbus OH USA Los Alamos National Laboratory Los Alamos NM USA

ISBN: (纸本)9781450319102

With growing computational capabilities of parallel machines, scientific simulations are being performed at finer spatial and temporal scales, leading to a data explosion. The growing sizes are making it extremely hard to store, manage, disseminate, analyze, and visualize these datasets, especially as neither the memory capacity of parallel machines, memory access speeds, nor disk bandwidths are increasing at the same rate as the computing power. Sampling can be an effective technique to address the above challenges, but it is extremely important to ensure that dataset characteristics are preserved, and the loss of accuracy is within acceptable levels. In this paper, we address the data explosion problems by developing a novel sampling approach, and implementing it in a flexible system that supports server-side sampling and data subsetting. We observe that to allow subsetting over scientific datasets, data repositories are likely to use an indexing technique. Among these techniques, we see that bitmap indexing can not only effectively support subsetting over scientific datasets, but can also help create samples that preserve both value and spatial distributions over scientific datasets. We have developed algorithms for using bitmap indices to sample datasets. We have also shown how only a small amount of additional metadata stored with bitvectors can help assess loss of accuracy with a particular subsampling level. Some of the other properties of this novel approach include: 1) sampling can be flexibly applied to a subset of the original dataset, which may be specified using a value-based and/or a dimension-based subsetting predicate, and 2) no data reorganization is needed, once bitmap indices have been generated. We have extensively evaluated our method with different types of datasets and applications, and demonstrated the effectiveness of our approach.

关键词： bitmap indexing data sampling big data

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An Efficient GPU Based parallel Algorithm for Image reconstruction

An Efficient GPU Based Parallel Algorithm for Image reconstr...

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2nd IEEE international Conference on parallel, distributed and Grid Computing (PDGC)

作者： Bajpai, Manish Kumar Munshi, Prabhat Gupta, Phalguni Indian Inst Technol Nucl Engn & Technol Programme Kanpur 208016 Uttar Pradesh India Indian Inst Technol Kanpur Dept Comp Sci & Engn Kanpur India

ISBN: (纸本)9781467329255;9781467329224

This paper proposes an efficient GPU based parallel algorithm image reconstruction. It has been implemented on a system having a general purpose graphical processing unit (GPU), Nvidia graphics card GTX-275. Experimental results reveal that an image of size 256 x 256 with 90 projections can be reconstructed in real time with single iteration. The results enable us to use MART algorithm for online applications.

关键词： Computed Tomography MART GPU

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