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2012 11th international conference on Information Science, Signal processing and their Applications, ISSPA 2012

作者： Montagnon, Emmanuel Hissoiny, Sami Despres, Philippe Cloutier, Guy 2099 Alexandre de Sève Montréal QC H2L 2W5 Canada Institute of Biomedical Engineering Université de Montréal Montréal QC H3C 3J7 Canada Department of Computer and Software Engineering ÉcolePolytechnique de Montréal Montréal QC H3T 1J4 Canada Montréal QC H2L 4M1 Canada Québec QC G1R 2J6 Canada Department of Radiology Radio-Oncology and Nuclear Medicine Université de Montréal Montréal QC H3T 1J4 Canada

ISBN: (纸本)9781467303828

this paper addresses the computational cost of the normalized cross-correlation (NCC) algorithm in ultrasound elastography. parallel implementations of the NCC algorithm based on multicore architectures and a graphical processor unit (GPU) are formulated and applied to radio-frequency (RF) data from dynamic elastography experiments. Compared to single computer processor unit (CPU) performances, results show that parallel implementation of the NCC algorithm allows speedups of less than 5 for multi-threaded execution on CPU and up to 85 using a GPU. processing frame rates from 80 to 173 sec 1 have been achieved for large fields of view with good spatial resolution. the trade-off between accuracy, spatial resolution and computational cost in displacement estimation using the NCC algorithm therefore appears obsolete. Open source codes for implementing the NCC algorithm on GPU are made available at ***. © 2012 IEEE.

关键词： parallel processing systems

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Topology-aware deployment of scientific applications in cloud computing

Topology-aware deployment of scientific applications in clou...

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2012 IEEE 5th international conference on Cloud Computing, CLOUD 2012

作者： Fan, Pei Chen, Zhenbang Wang, Ji Zheng, Zibin Lyu, Michael R. National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Dept. of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780769547558

Nowadays, more and more scientific applications are moving to cloud computing. the optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually topology-aware applications. therefore, considering the topology of a scientific application during the development will benefit the performance of the application. However, it is challenging to automatically discover and make use of the communication pattern of a scientific application while deploying the application on cloud. To attack this challenge, in this paper, we propose a framework to discover the communication topology of a scientific application by pre-execution and multi-scale graph clustering, based on which the deployment can be optimized. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. the experimental results show the effectiveness of our topology-aware deployment method. © 2012 IEEE.

关键词： Cloud computing

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Advanced High Speed parallel Frame Synchronizer for High Data Rate Indian Remote Sensing Satellite Series

Advanced High Speed Parallel Frame Synchronizer for High Dat...

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5th international conference on Computers and Devices for Communication (CODEC)

作者： Srinivas, R. Nithyanandan, L. Rao, P. V. V. Subba Kumar, P. Naveen Umadevi, G. ISRO Natl Remote Sensing Ctr Hyderabad Andhra Pradesh India Pondicherry Engn Coll Dept Elect & Commun Engg Pondicherry India Osmania Univ Dept Elect & Commun Engg Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467326209;9781467326193

the Video data from remote sensing satellite is transmitted serially frame after frame. To mark the beginning of video data a known unique code is embedded at the beginning of the video data frame. this is known as Frame Synchronizer code. Pseudo-Noise sequences are chosen as frame synchronizer codes as they are easy to generate, detect and have auto correlation property. Generally, for all Indian Remote sensing satellite series 127 bits Pseudo-Noise sequence is chosen as frame synchronizer code. On-board the satellite, the video data buffering is in serial fashion and for appropriate processing it is required to be formatted in data packets. Hence, data formatting is done on-board the satellite and then the Pulse Coded Modulated data is transmitted to ground station. the previous Indian Remote Sensing Satellites series has data rate of 105 Mbps. the Present data rate of Radar imaging satellite-1 (RISAT-1) is 640MBps. In order to accommodate such high data rates in the limited bandwidths, the video data is transmitted in Right hand Circular Polarization (RHCP) and Left Hand Circular Polarization (LHCP). the QPSK modulation is used for video data transmission. Hence at such high data rates an advanced high speed frame synchronizer is required to cater to such high data rates. the advanced parallel processing technique of frame synchronism, detection and monitoring is unique as it doubles the operating frequency of the Frame synchronizer. Also with the same hardware, the frequency response of the hardware circuit can be doubled. If the maximum operating frequency is 'f' Hz then using Advanced parallel Frame synchronizer technique, the operating frequency response is doubled to '2f' Hz using the same hardware chip. In this paper Advanced frame synchronizer circuit is been implemented and is put to use to archive video data from Indian Radar Imaging Satellite-1.

关键词： Advanced Frame synchronizer Pseudo-noise Sequence (PN sequence) Indian remote sensing satellites (IRS) Right hand circular polarization (RHCP) Left hand circular polarization (LHCP) Radar Imaging satellite (RISAT)

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CloudBay: Enabling an Online Resource Market Place for Open Clouds

CloudBay: Enabling an Online Resource Market Place for Open ...

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5th IEEE/ACM international conference on Utility and Cloud Computing (UCC)

作者： Zhao, Han Yu, Ze Tiwari, Shivam Mao, Xing Lee, Kyungyong Wolinsky, David Li, Xiaolin Figueiredo, Renato Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA

ISBN: (纸本)9780769548623;9781467344326

this paper presents CloudBay, an online resource trading and leasing platform for multi-party resource sharing. Following a market-oriented design principle, CloudBay provides an abstraction of a shared virtual resource space across multiple administration domains, and features enhanced functionalities for scalable and automatic resource management and efficient service provisioning. CloudBay distinguishes itself from existing research and contributes in a number of aspects. First, it leverages scalable network virtualization and self-configurable virtual appliances to facilitate resource federation and parallel application deployment. Second, CloudBay adopts an eBay-style transaction model that supports differentiated services with different levels of job priorities. For cost-sensitive users, CloudBay implements an efficient matchmaking algorithm based on auction theory and enables opportunistic resource access through preemptive service scheduling. the proposed CloudBay platform stands between HPC service sellers and buyers, and offers a comprehensive solution for resource advertising and stitching, transaction management, and application-to-infrastructure mapping. In this paper, we present the design details of CloudBay, and discuss lessons and challenges encountered in the implementation process. the proof-of-concept prototype of CloudBay is justified through experiments across multiple sites and simulations.

关键词： advertising cloud computing leasing parallel processing resource allocation scheduling transaction processing virtualisation

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A Four Quadrants parallel-Recursive 2-D DCT/IDCT VLSI Architecture

A Four Quadrants Parallel-Recursive 2-D DCT/IDCT VLSI Archit...

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5th international conference on Emerging Trends in Engineering and Technology (ICETET)

作者： Purwita, Ardimas Andi Adiono, Trio School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Jalan Ganesha 10 Indonesia

ISBN: (纸本)9780769548845;9781479902767

A 2D DCT/IDCT is widely used in image compression system. However, due to its computational intensif, dedicated hardware architecture is required to compress large video data in real-time. this paper propose an efficient architecture to concurently process all macroblock data in 2D DCT/IDCT. As a result, the processing speed is increased up to 13.30 times and no transposition buffer is required. Eliminating transposition buffer is significantly reduced the design size and the processing latency. Moreover, proposed architecture critical path consists only three stage adder which improve system speed. the proposed architecture also does not require a large bit width to produce high quality reconstructed image (High PSNR). It is because the architecture use the multiplier just once for each data. therefore, the rounding of intermediate data does not cumulative. the design has been implemented and verified in FPGA to real-time show compression and decompression of a moving JPEG. the design has been also synthesized using CMOS 0.18 mu m technology library that results in 12.37 ns critical path.

关键词： Image compression DCT Four Quadrants Semi-parallel-Recursive transposition buffer fast small

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Accelerating the dynamic programming for the optimal polygon triangulation on the GPU

Accelerating the dynamic programming for the optimal polygon...

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12th international conference on algorithms and architectures for parallel processing, ICA3PP 2012

作者： Nishida, Kazufumi Nakano, Koji Ito, Yasuaki Department of Information Engineering Hiroshima University Kagamiyama 1-4-1 Higashi Hiroshima 739-8527 Japan

ISBN: (纸本)9783642330773

Modern GPUs (Graphics processing Units) can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). the optimal polygon triangulation problem for a convex polygon is an optimization problem to find a triangulation with minimum total weight. It is known that this problem can be solved using the dynamic programming technique in O(n 3) time using a work space of size O(n 2). the main contribution of this paper is to present an efficient parallel implementation of this O(n 3)-time algorithm on the GPU. In our implementation, we have used two new ideas to accelerate the dynamic programming. the first idea (granularity adjustment) is to partition the dynamic programming algorithm into many sequential kernel calls of CUDA, and to select the best size and number of blocks and threads for each kernel call. the second idea (sliding and mirroring arrangements) is to arrange the temporary data for coalesced access of the global memory in the GPU to minimize the memory access overhead. Our implementation using these two ideas solves the optimal polygon triangulation problem for a convex 16384-gon in 69.1 seconds on the NVIDIA GeForce GTX 580, while a conventional CPU implementation runs in 17105.5 seconds. thus, our GPU implementation attains a speedup factor of 247.5. © 2012 Springer-Verlag.

关键词： Graphics processing unit

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HSPp-BLAST: Highly scalable parallel PSI-BLAST for very large-scale sequence searches

HSPp-BLAST: Highly scalable parallel PSI-BLAST for very larg...

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4th international conference on Bioinformatics and Computational Biology 2012, BICoB 2012

作者： Rekepalli, Bhanu Vose, Aaron Giblock, Paul Joint Institute for Computational Sciences University of Tennessee Oak Ridge National Laboratory Oak Ridge TN 37831 United States

ISBN: (纸本)9781618397461

Based on recent published articles, the growth of genomic data has overtaken and outpaced both performance improvements of storage technologies and processing power due to the revolutionary advancements of next generation sequencing technologies. By bringing down the costs and increasing throughput by many orders of magnitude with sequencing technologies, data is doubling every 9 months resulting in the exponential growth of genomic data in recent years. However, data analysis becomes increasingly difficult and can be prohibitive, as existing bioinformatics tools developed in the past decade focus mainly on desktops, workstations and small clusters that have limited capabilities. Improving the performance and scalability of such tools is critical to transforming ever-growing raw genomic data into biological knowledge containing invaluable information directly related to human health. this paper describes a new software application which includes optimization techniques improving the scalability of a most widely used bioinformatics tool "PSI-BLAST" on advanced parallel architectures, pushing the envelope of biological data analysis. We show that our improvements allow near-linear scaling to tens of thousands of processing cores, up to the maximum non-capability size on current petaflop supercomputers. this new tool increases by 5 orders of magnitude the amount of genomics data that can be processed per hour.

关键词： Supercomputers

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parallel-META: efficient metagenomic data analysis based on high-performance computation

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BMC SYSTEMS BIOLOGY 2012年第1期6卷 1-11页

作者： Su, Xiaoquan Xu, Jian Ning, Kang Chinese Acad Sci Qingdao Inst Bioenergy & Bioproc Technol Qingdao Shandong Peoples R China

Background: Metagenomics method directly sequences and analyses genome information from microbial communities. there are usually more than hundreds of genomes from different microbial species in the same community, and the main computational tasks for metagenomic data analyses include taxonomical and functional component examination of all genomes in the microbial community. Metagenomic data analysis is both data- and computation-intensive, which requires extensive computational power. Most of the current metagenomic data analysis softwares were designed to be used on a single computer or single computer clusters, which could not match with the fast increasing number of large metagenomic projects' computational requirements. therefore, advanced computational methods and pipelines have to be developed to cope with such need for efficient analyses. Result: In this paper, we proposed parallel-META, a GPU- and multi-core-CPU-based open-source pipeline for metagenomic data analysis, which enabled the efficient and parallel analysis of multiple metagenomic datasets and the visualization of the results for multiple samples. In parallel-META, the similarity-based database search was parallelized based on GPU computing and multi-core CPU computing optimization. Experiments have shown that parallel-META has at least 15 times speed-up compared to traditional metagenomic data analysis method, with the same accuracy of the results http://***/***. Conclusion: the parallel processing of current metagenomic data would be very promising: with current speed up of 15 times and above, binning would not be a very time-consuming process any more. therefore, some deeper analysis of the metagenomic data, such as the comparison of different samples, would be feasible in the pipeline, and some of these functionalities have been included into the parallel-META pipeline.

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Billion-Particle SIMD-Friendly Two-Point Correlation on Large-Scale HPC Cluster Systems

Billion-Particle SIMD-Friendly Two-Point Correlation on Larg...

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25th ACM/IEEE international conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Chhugani, Jatin Kim, Changkyu Shukla, Hemant Park, Jongsoo Dubey, Pradeep Shalf, John Simon, Horst D. Intel Corp Parallel Comp Lab Santa Clara CA 95051 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9781467308052;9781467308045

Two-point Correlation Function (TPCF) is widely used in astronomy to characterize the distribution of matter/energy in the Universe, and help derive the physics that can trace back to the creation of the universe. However, it is prohibitively slow for current sized datasets, and would continue to be a critical bottleneck with the trend of increasing dataset sizes to billions of particles and more, which makes TPCF a compelling benchmark application for future exa-scale architectures. State-of-the-art TPCF implementations do not map well to the underlying SIMD hardware, and also suffer from load-imbalance for large core counts. In this paper, we present a novel SIMD-friendly histogram update algorithm that exploits the spatial locality of histogram updates to achieve near-linear SIMD scaling. We also present a load-balancing scheme that combines domain-specific initial static division of work and dynamic task migration across nodes to effectively balance computation across nodes. Using Zin supercomputer at Lawrence Livermore National Laboratory (25,600 cores of Intel (R) Xeon (R) E5-2670, each with 256-bit SIMD), we achieve 90% parallel efficiency and 96% SIMD efficiency, and perform TPCF computation on a 1.7 billion particle dataset in 5.3 hours (at least 35x faster than previous approaches). In terms of cost per performance (measured in flops/$), we achieve at least an order-of-magnitude (11.1x) higher flops/$ as compared to the best known results [1]. Consequently, we now have line-of-sight to achieving the processing power for correlation computation to process billion+ particles telescopic data.

关键词： astronomy computing benchmark testing correlation methods mainframes parallel architectures performance evaluation resource allocation

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TOWARDS LARGE SCALE AUTOMATED INTERPRETATION OF CYTOGENETIC BIODOSIMETRY DATA

TOWARDS LARGE SCALE AUTOMATED INTERPRETATION OF CYTOGENETIC ...

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IEEE 6th international conference on Information and Automation for Sustainability (ICIAFS)

作者： Li, Yanxin Wickramasinghe, Asanka Subasinghe, Akila A. Samarabandu, Jagath Knoll, Joan Wilkins, Ruth Flegal, Farrah Rogan, Peter Western Univ London ON Canada Cytognomix Inc London ON Canada Hlth Canada London ON Canada Atom Energy Canada Ltd London ON Canada

ISBN: (纸本)9781467319751;9781467319768

Cytogenetic biodosimetry is the definitive test for assessing exposure to ionizing radiation. It involves manual assessment of the frequency of dicentric chromosomes (DCs) on a microscope slide, which potentially contains hundreds of metaphase cells. We developed an algorithm that can automatically and accurately locate centromeres in DAPI-stained metaphase chromosomes and that will detect DCs. In this algorithm, a set of 200-250 metaphase cell images are ranked and sorted. the 50 top-ranked images are used in the triage DC assay (DCA). To meet the requirement of DCA in a mass casualty event, we are accelerating our algorithm through parallelization. In this paper, we present our finding in accelerating our ranking and segmentation algorithms. Using data parallelization on a desktop system, the ranking module was up to 4-fold faster than the serial version and the Gradient Vector Flow module (GVF) used in our segmentation algorithm was up to 8-fold faster. Large scale data parallelization of the ranking module processed 18,694 samples in 11.40 hr. Task parallelization of Image ranking with parallelized labeling on a desktop computer reduced processing time by 20% of a serial process, and GVF module recoded with parallelized matrix inversion reduced time by 70%. Overall, we estimate that the automated DCA will require around 1 min per sample on a 64-core computing system. Our long-term goal is to implement these algorithms on a high performance computer cluster to assess radiation exposures for thousands of individuals in a few hours.

关键词： cellular biophysics dosimetry genetics image segmentation matrix inversion medical image processing parallel processing

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