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parallel LDPC Decoding on a GPU using OpenCL and Global Memory for Accelerators 10

Parallel LDPC Decoding on a GPU using OpenCL and Global Memo...

引用

IEEE international conference on Networking, Architecture and Storage (NAS 2015)

作者： Hong, Jung-Hyun Chung, Ki-Seok Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

ISBN: (纸本)9781467378918

This paper introduces a parallel software decoder of Low Density Parity Check ( LDPC) codes with an Open Computing Language ( OpenCL) framework including Global Memory for ACcelerators ( GMAC). The LDPC code is one of the most popular and strongest error correcting codes for mobile communication systems. OpenCL is an open standard programming framework that supports programming languages and application programming interfaces ( APIs) for heterogeneous platforms. GMAC is a software implementation of Asymmetric distributed Shared Memory ( ADSM) that maintains a shared logical memory space for the host to access memory objects in the physical memory of an OpenCL device. In this paper, we parallelize the iterative LDPC decoding steps on a graphics processing unit ( GPU) using OpenCL. To improve the performance of the proposed decoder, data transfer optimization techniques between the host and the GPU including pre-pinned OpenCL memory objects for GMAC are applied. In terms of the entire decoding time, the speedup of the proposed LDPC decoder over a conventional OpenCL implementation is 1.28.

关键词： OpenCL GMAC LDPC decoder

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parallelizing Natural Language techniques for Knowledge Extraction from Cloud Service Level Agreements 3

Parallelizing Natural Language Techniques for Knowledge Extr...

引用

IEEE international conference on Big Data

作者： Mittal, Sudip Joshi, Karuna P. Pearce, Claudia Joshi, Anupam Univ Maryland Baltimore MD 21250 USA

ISBN: (纸本)9781479999255

To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our prototype system that uses a Hadoop cluster to extract knowledge from unstructured legal text documents. For this prototype we have considered publicly available SLA/terms of service documents of various cloud providers. We use established natural language processing techniques in parallel to speed up cloud legal knowledge base creation. Our system considerably speeds up knowledge base creation and can also be used in other domains that have unstructured data.

关键词： Knowledge Extraction distributed Systems Data Mining

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DistEQ: distributed Equality Query processing on Encoded Bitmap Index 12

DistEQ: Distributed Equality Query Processing on Encoded Bit...

引用

12th int joint conf comp sci software engn (Jcsse)

作者： Keawpibal, Naphat Duangsuwan, Jarunee Wettayaprasit, Wiphada Preechaveerakul, Ladda Vanichayobon, Sirirut Prince Songkla Univ Fac Sci Dept Comp Sci Hat Yai Thailand

ISBN: (纸本)9781479919666

With the next generation of data, called Big Data, an amount of data has grown rapidly, not only size but also variety and the speed of changing contents in its. The difficulties in Big Data are handling to store and process a large amount of data in real-time. Bitmap Indexes have widely known method to improve processing time. The advantage of these indexes is utilization of low-cost Boolean operations on the index before accessing to raw data. In this paper, we proposed a distributed and parallel processing, called DistEQ, to improve equality query on Encoded Bitmap Index by using MapReduce framework. Our comparative study demonstrates that our proposed approach outperforms other existing Encoded bitmap indexing techniques for an equality query in term of scalability and space-time trade-off.

关键词： bitmap index encoded bitmap index equality query mapreduce

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A Hypergraph Based Task Scheduling Strategy for Massive parallel Spatial Data processing on Master-Slave Platforms 23

A Hypergraph Based Task Scheduling Strategy for Massive Para...

引用

23rd international conference on Geoinformatics (Geoinformatics)

作者： Cheng, Bo Guan, Xuefeng Wu, Huayi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (纸本)9781467376631

Data intensive applications are now ubiquitous in many fields, e.g. high-energy physics, astronomy, climate modeling, and the geosciences. Especially for spatial data processing in the distributed platforms, the parallel tasks are generally required to handle massive spatial datasets and usually treated as data-intensive applications. In order to address the challenge of massive spatial data processing, we propose a hypergraph based tasks scheduling strategy on a master-slave platform. Our task scheduling strategy involves two consecutive steps: mapping and scheduling. In the mapping process, we formulate a hypergraph partition model to decide which tasks will be executed by each slave processor. At the same time, the scheduling process determines the execution order of selected tasks and the order in which the master transfers the files to the slaves. An experiment based on the GridSim toolkit was conducted to evaluate and compare our scheduling algorithm with Min-min heuristic. Our experimental results show that our scheduling strategy outperforms the Min-min heuristic.

关键词： task scheduling hypergraph spatial data processing master-slave platform

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Revealing Potential Performance Improvements By Utilizing Hybrid Work-Sharing For Resource-Intensive Seismic applications 23

Revealing Potential Performance Improvements By Utilizing Hy...

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

作者： Siegl, Patrick Buchty, Rainer Berekovic, Mladen Tech Univ Carolo Wilhelmina Braunschweig Chair Chip Design Embedded Comp C3E Braunschweig Germany

ISBN: (纸本)9781479984909

Heterogeneous system architectures are becoming more and more of a commodity in the scientific community. While it remains challenging to fully exploit such architectures, the benefits in performance and hybrid speed-up, by using a host processor and accelerators in parallel in a non-monolithic matter, are significant. Hereby, the energy efficiency is becoming an increasingly critical challenge for future high-performance computing (HPC) systems, which do want to exceed the Exascale barrier with several competing architecture concepts ranging from high-performance CPUs, combined with GPUs acting as floating-point accelerators, to computationally weak CPUs, paired with dedicated and highly-performant FPGA-based accelerators. In this paper, we realize and evaluate a hybrid computing approach based on a two-dimensional seismic streaming algorithm with several heterogeneous system architectures, including conventional HPC approaches based on powerful CPUs and GPUs. Furthermore, we elaborate the effort on an embedded system platform claiming to be a "mini supercomputer" [1]. Several CPU and accelerator combinations are utilized in a manual work-sharing manner with the aim of achieving significant performance speed-ups and a detailed energy-efficiency study. Based on roofline models and experimental evaluations, the paper provides an insight into the fact that hybrid computing is mostly unconditionally beneficial for balanced systems regarding the performance as well as the energy efficiency, aiding the programmer in the decision whether or not costly, manually tuned, homogeneous implementations are worthwhile.

关键词： narrow stencil computing (embedded) hybrid work-sharing multi GPU processing performance evaluation

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Analyzing the Effects of Reordering Work List Items for Selected Control Flow Patterns 19

Analyzing the Effects of Reordering Work List Items for Sele...

引用

IEEE 19th international Enterprise distributed Object Computing (EDOCW)

作者： Pflug, Johannes Rinderle-Ma, Stefanie Univ Vienna Res Grp Workflow Syst & Technol Vienna Austria

ISBN: (纸本)9781467393317

Efficient resource management is an important requirement for many process-oriented applications. Typically, work items are assigned to resources through their work lists. There are many reasons for reordering work items in a resource's work list. For process scheduling, for example, swapping process instances constitutes a mean to keep due times. At the same time, reducing the throughput time of the global process is typically not the primary goal. For process optimization, in turn, the implications of reordering work items on the overall temporal performance of the process might be crucial. In this paper, we investigate how reordering work items affects performance parameters that are typically associated with a first-in-first-out processing mechanism at resources. The analysis is conducted for single process tasks and for typical control flow patterns such as sequence as well as parallel and alternative branchings. It is shown that the implications on the global throughput time are less than expected, while the effects on instance-based parameters strongly depend on the control-flow pattern in which the reordering mechanism is implemented. The results are supported by means of a simulation.

关键词： information systems resource allocation scheduling alternative branchings control flow patterns first-in-first-out processing mechanism instance based parameters parallel branchings process optimization process scheduling resource management work list items reordering mechanism Delays Electronic mail Engines Optimization Process control Throughput Yttrium Business Process Analysis Business Process Management Business Process Optimization Work List Management management of resources FLOW PATTERNS Process optimization Business process management electronic mail throughput time Engines Process control Rare earths yttrium Throughput Computer based information systems Resource utilization delay

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Hardware Efficient AES for Image processing with High Throughput 1

Hardware Efficient AES for Image Processing with High Throug...

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1st international conference on Next Generation Computing Technologies (NGCT)

作者： Delakoti, Neha Gaur, Nidhi Mehra, Anu Amity Univ ASET Dept ECE Noida India

ISBN: (纸本)9781467368094

Nowdays, image processing is applied to send an enhanced image in all applications including forensics, robotics, military communications. However, these applications have a additional overhead of image security. AES is one of the high speed technique which is used widely against various attacking techniques inspite of its high computational complexity. In this paper we propose the novel implementation of AES(Advance encryption standard) algorithm with reduced coding complexity and enhanced throughput by parallel processing of the key expansion technique. In addition, proposed approach also reduces the hardware required for implementation of AES. Algorithm is implemented on Xilinx virtex-6 using Questasim 10.0 b and further the encryption and decryption of image is simulated in MATLAB 2011a.

关键词： AES attacking encryption decryption

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distributed Range-Based Meta-Data Management for an In-Memory Storage

Distributed Range-Based Meta-Data Management for an In-Memor...

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21st international conference on parallel and distributed Computing (Euro-Par)

作者： Klein, Florian Beineke, Kevin Schoettner, Michael Univ Dusseldorf Inst Informat Univ Str 1 D-40225 Dusseldorf Germany

ISBN: (纸本)9783319273082;9783319273075

Large-scale interactive applications and online graph processing require fast data access to billions of small data objects. DXRAM addresses this challenge by keeping all data always in RAM of potentially many nodes aggregated in a data center. Such storage clusters need a space-efficient and fast meta-data management. In this paper we propose a range-based meta-data management allowing fast node lookups while being space efficient by combining data object IDs in ranges. A super-peer overlay network is used to manage these ranges together with backup-node information allowing parallel and fast recovery of meta data and data of failed peers. Furthermore, the same concept can also be used for client-side caching. The measurement results show the benefits of the proposed concepts compared to other meta-data management strategies as well as its very good overall performance evaluated using the social network benchmark BG.

关键词： Metadata

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distributed Top-k Query processing on Multi-dimensional Data with Keywords 15

Distributed Top-k Query Processing on Multi-dimensional Data...

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27th international conference on Scientific and Statistical Database Management (SSDBM)

作者： Amagata, Daichi Hara, Takahiro Nishio, Shojiro Osaka Univ Grad Sch Informat Sci & Technol Dept Multimedia Engn Suita Osaka 565 Japan

ISBN: (纸本)9781450337090

As we are in the big data era, techniques for retrieving only user desirable data objects from massive and diverse datasets is being required. Ranking queries, e.g., top-k queries, which rank data objects based on a user-specified scoring function, enable to find such interesting data for users, and have received significant attention due to its wide range of applications. While many techniques for both centralized and distributed top-k query processing have been developed, they do not consider query keywords, i.e., simply retrieving k data with the best score. Utilizing keywords, on the other hand, is a common approach in data (and information) retrieval. Despite of this fact, there is no study on retrieving top-k data containing all query keywords. We define, in this paper, a new query which enriches the conventional top-k queries, and propose some algorithms to solve the novel problem of how to efficiently retrieve k data objects with the best score and all query keywords from distributed databases. Extensive experiments on both real and synthetic data have demonstrated the efficiency and scalability of our algorithms in terms of communication cost and running time.

关键词： Big data

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Spark-Gram: Mining Frequent N-grams Using parallel processing in Spark

Spark-Gram: Mining Frequent N-grams Using Parallel Processin...

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international conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Utama, Prasetya Ajie Distiawan, Bayu Univ Indonesia Fac Comp Sci Jakarta Indonesia

ISBN: (纸本)9781509003631

Mining sequence patterns in form of n-grams (sequences of words that appear consecutively) from a large text data is one of the fundamental parts in several information retrieval and natural language processing applications. In this work, we present Spark-gram, a method for large scale frequent sequence mining based on Spark that was adapted from its equivalent method in MapReduce called Suffix-sigma. Spark-gram design allows the discovery of all n-grams with maximum length sigma and minimum occurrence frequency tau, using iterative algorithm with only a single shuffle phase. We show that Spark-gram can outperform Suffix-sigma mainly when tau is high but potentially worse when the value of s grows higher.

关键词： distributed computing hadoop mapreduce spark text mining

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