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9th International Conference on Natural Computation (ICNC)

作者： Luan, Hua Zhou, Mingquan Fu, Yan Beijing Normal Univ Beijing 100875 Peoples R China

ISBN: (纸本)9781467347143

Nowadays, the volume of multimedia and unstructured data has grown rapidly. More and more three-dimensional (3D) models are created for ever increasing applications. New storage and processing technologies are needed to keep pace with the continuous growth of big data. Hadoop is an attractive and open-source platform for large-scale data storage and analytics. Our previous research work has applied Hadoop distributed file system to efficiently manage 3D data for a 3D model retrieval system. To take better advantages of Hadoop, in this paper we propose two parallel strategies to improve the storing and accessing performance of 3D models. The MapReduce paradigm is adopted to provide a coarse grained parallelism for data loading, and a lightweight multithreaded algorithm is presented for data accesses. We conduct an extensive performance study on a cluster and the results show that significant performance increase can be gained for the parallel techniques.

关键词： 3D models Hadoop MapReduce parallel techniques

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Using of parallel techniques for Block Algorithms

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JOURNAL OF AUTOMATION AND INFORMATION SCIENCES 2011年第6期43卷 74-80页

作者： Falfushinsky, V. V. Natl Acad Sci Ukraine VM Glushkov Cybernet Inst Kiev Ukraine

The examples of the different hardware architectures of computing devices and software tools are considered. The structure of hardware and software architecture of computing devices are described. The comparative characteristic of the run-time of two block algorithms on different hardware platform of computing devices. It is found that the main criterion while construction of he algorithms is an optimal memory usage and delays minimizing while exchanging data between a host computer and a computing node device.

关键词： parallel techniques block algorithms structure of hardware and software architecture comparative characteristic of the run-time optimal memory usage and minimizing delays

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Dynamic-CoMPI: dynamic optimization techniques for MPI parallel applications

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JOURNAL OF SUPERCOMPUTING 2012年第1期59卷 361-391页

作者： Filgueira, Rosa Carretero, Jesus Singh, David E. Calderon, Alejandro Nunez, Alberto Univ Carlos III Madrid Dept Comp Sci Madrid Spain

This work presents an optimization of MPI communications, called Dynamic-CoMPI, which uses two techniques in order to reduce the impact of communications and non-contiguous I/O requests in parallel applications. These techniques are independent of the application and complementaries to each other. The first technique is an optimization of the Two-Phase collective I/O technique from ROMIO, called Locality aware strategy for Two-Phase I/O (LA-Two-Phase I/O). In order to increase the locality of the file accesses, LA-Two-Phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal I/O data communication schedule. The main purpose of this technique is the reduction of the number of communications involved in the I/O collective operation. The second technique, called Adaptive-CoMPI, is based on run-time compression of MPI messages exchanged by applications. Both techniques can be applied on every application, because both of them are transparent for the users. Dynamic-CoMPI has been validated by using several MPI benchmarks and real HPC applications. The results show that, for many of the considered scenarios, important reductions in the execution time are achieved by reducing the size and the number of the messages. Additional benefits of our approach are the reduction of the total communication time and the network contention, thus enhancing, not only performance, but also scalability.

关键词： MPI library parallel techniques Clusters architectures Compression algorithms Collective I/O Adaptive systems Heuristics

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parallel EXPERIENCE ON THE INVERSE MATRIX COMPUTATION

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parallel COMPUTING 1991年第8期17卷 907-912页

作者： FRANCOMANO, E PECORELLA, A MACALUSO, AT UNIV PALERMO DIPARTIMENTO INGN ELETTRI-90134 PALERMOITALY

In this paper a new structure of computation to evaluate the inverse matrix has been performed, composing in an opportune way the bordering-method and the recursive partitioning method. The scheme reported is based on the block decomposition and on the elimination of the recursion in the process. The algorithm has been realized in a parallel way on the CRAY Y-MP 8/432 and the multitasking techniques have been used.

关键词： INVERSE MATRIX CRAY Y-MP 8/432 parallel techniques MULTITASKING MICROTASKING MACROTASKING AUTOTASKING

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parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

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JOURNAL OF SUPERCOMPUTING 2012年第3期59卷 1455-1477页

作者： Shahbahrami, Asadollah Pham, Tuan Anh Bertels, Koen Univ Guilan Dept Comp Engn Fac Engn Rasht Iran Delft Univ Technol Comp Engn Lab Fac EEMCS NL-2628 CD Delft Netherlands

Texture features extraction algorithms are key functions in various image processing applications such as medical images, remote sensing, and content-based image retrieval. The most common way to extract texture features is the use of Gray Level Co-occurrence Matrices (GLCMs). The GLCM contains the second-order statistical information of spatial relationship of the pixels of an image. Haralick texture features are extracted using these GLCMs. However, the GLCMs and Haralick texture features extraction algorithms are computationally intensive. In this paper, we apply different parallel techniques such as task- and data-level parallelism to exploit available parallelism of those applications on the Cell multi-core processor. Experimental results have shown that our parallel implementations using 16 Synergistic Processor Elements significantly reduce the computational times of the GLCMs and texture features extraction algorithms by a factor of 10x over non-parallel optimized implementations for different image sizes from 128x128 to 1024x1024.

关键词： Texture feature extraction Co-occurrence matrix parallel techniques Cell architecture

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OBMA: Scalable Route Lookups With Fast and Zero-Interrupt Updates

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第6期32卷 4842-4854页

作者： Zhang, Chuwen Feng, Yong Song, Haoyu Wan, Ying Xu, Wenquan Liu, Bin Tsinghua Univ Dept Comp Sci Beijing 100190 Peoples R China Futurewei Technol San Jose CA 95131 USA Tsinghua Univ Dept Comp Sci & Technol Beijing 100190 Peoples R China

Software-based IP route lookup is a key component for packet forwarding in Software Defined Networks. Running lookup algorithms on commodity CPUs is flexible and scalable, which shows advantages on cost and power consumption over the hardware-based forwarding engines. However, dynamic network functions and services make route updates more frequent than ever. Existing algorithms often fall short of the incremental update requirements. In this paper, we propose the Overlay BitMap Algorithm (OBMA), which contains several variations, to support extraordinary update performance while maintaining the highest-in-class lookup speed and storage efficiency. Starting from the basic OBMA_B, we develop two variations with different tradeoffs for different application scenarios. OBMA_L supports faster lookups than OBMA_B at a small cost of update speed. OBMA_S achieves better storage efficiency than OBMA_B at a small cost of lookup throughput. We run our algorithms on a commodity CPU and evaluate them with real-world route tables and traces. The experiments show that OBMA achieves the lowest memory footprint, the highest update speed, and over 200 Mpps lookup throughput. Specifically, OBMA_S reduces the memory footprint to 3.98 bytes/prefix which is 25.33% smaller that of the state-of-the-art Poptrie;OBMA_L supports 252.02 Mpps lookup throughput with a single thread, and more than 600 Mpps with multiple parallel threads in a single CPU, significantly outperforming the state-of-the-art Poptrie and SAIL;OBMA_B supports updates at a rate of 14.58M updates/s which is 15 times faster than Poptrie. The tests show that the update process has little interference with the lookup process for OBMA, and achieves zero-interrupt to lookups with multiple threads.

关键词： Throughput Software algorithms Memory management Data structures Costs IP networks Graphics processing units Filtering algorithms Instruction sets Filtering theory IP lookup bitmap parallel techniques

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Ultra-Fast Bloom Filters using SIMD techniques

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2019年第4期30卷 953-964页

作者： Lu, Jianyuan Wan, Ying Li, Yang Zhang, Chuwen Dai, Huichen Wang, Yi Zhang, Gong Liu, Bin Tsinghua Univ Beijing 100084 Peoples R China Alibaba Cloud Beijing 100102 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Huawei Future Network Theory Lab Hong Kong Peoples R China

The network link speed is growing at an ever-increasing rate, which requires all network functions on routers/switches to keep pace. Bloom filter is a widely-used membership check data structure in networking applications. Correspondingly, it also faces the urgent demand of improving the performance in membership check speed. To this end, this paper proposes a new Bloom filter variant called Ultra-Fast Bloom Filters (UFBF), by leveraging the Single Instruction Multiple Data (SIMD) techniques. We make three improvements for UFBF to accelerate the membership check speed. First, we develop a novel hash computation algorithm which can compute multiple hash functions in parallel with the use of SIMD instructions. Second, we elaborate a Bloom filter's bit-test process from sequential to parallel, enabling more bit-tests per unit time. Third, we improve the cache efficiency of membership check by encoding an element's information to a small block so that it can fit into a cache-line. We further generalize UFBF, called c-UFBF, to make UFBF supporting large number of hash functions. Both theoretical analysis and extensive evaluations show that the UFBF greatly outperforms the state-of-the-art Bloom filter variants on membership check speed.

关键词： Bloom filter SIMD parallel techniques

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Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture

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COMPUTATIONAL ECONOMICS 2016年第2期48卷 253-280页

作者： Muresano, Ronal Pagano, Andrea Commiss European Communities IPSC JRC Financial & Econ Anal Unit Via E Fermi 2749 I-21027 Ispra Italy

Currently, multi-core system is a predominant architecture in the computational word. This gives new possibilities to speedup statistical and numerical simulations, but it also introduce many challenges we need to deal with. In order to improve the performance metrics, we need to consider different key points as: core communications, data locality, dependencies, memory size, etc. This paper describes a series of optimization steps done on the SYMBOL model meant to enhance its performance and scalability. SYMBOL is a micro-funded statistical tool which analyses the consequences of bank failures, taking into account the available safety nets, such as deposit guarantee schemes or resolution funds. However, this tool, in its original version, has some computational weakness, because its execution time grows considerably, when we request to run with large input data (e.g. large banking systems) or if we wish to scale up the value of the stopping criterium, i.e. the number of default scenarios to be considered. Our intention is to develop a tool (extendable to other model having similar characteristics) where a set of serial (e.g. deleting redundancies, loop enrolling, etc.) and parallel strategies (e.g. OpenMP, and GPU programming) come together to obtain shorter execution time and scalability. The tool uses automatic configuration to make the best use of available resources on the basis of the characteristics of the input datasets. Experimental results, done varying the size of the input dataset and the stopping criterium, show a considerable improvement one can obtain by using the new tool, with execution time reduction up to 96 % of with respect to the original serial version.

关键词： Optimization algorithm parallel techniques OpenMP GPU SYMBOL

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Combining parallel Stochastic Methods and Mixed Termination Rules in Optimization

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ALGORITHMS 2024年第9期17卷 394页

作者： Charilogis, Vasileios Tsoulos, Ioannis G. Gianni, Anna Maria Univ Ioannina Dept Informat & Telecommun Arta 47100 Greece

parallel optimization enables faster and more efficient problem-solving by reducing computational resource consumption and time. By simultaneously combining multiple methods, such as evolutionary algorithms and swarm-based optimization, effective exploration of the search space and achievement of optimal solutions in shorter time frames are realized. In this study, a combination of termination criteria is proposed, utilizing three different criteria to end the algorithmic process. These criteria include measuring the difference between optimal values in successive iterations, calculating the mean value of the cost function in each iteration, and the so-called "DoubleBox" criterion, which is based on the relative variance of the best value of the objective cost function over a specific number of iterations. The problem is addressed through the parallel execution of three different optimization methods (PSO, Differential Evolution, and Multistart). Each method operates independently on separate computational units with the goal of faster discovery of the optimal solution and more efficient use of computational resources. The optimal solution identified in each iteration is transferred to the other computational units. The proposed enhancements were tested on a series of well-known optimization problems from the relevant literature, demonstrating significant improvements in convergence speed and solution quality compared to traditional approaches.

关键词： global optimization parallel techniques termination rules evolutionary techniques

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An Adaptive, Scalable, and Portable Technique for Speeding Up MPI-Based Applications

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18th International Conference on Euro-Par parallel Processing

作者： Filgueira, Rosa Atkinson, Malcolm Nunez, Alberto Fernandez, Javier Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Univ Complutense Madrid Dept Sistemas Informat Computac Madrid 28040 Spain Univ Carlos III Madrid Dept Arquitectura Comp Leganes 3028911 Spain

ISBN: (纸本)9783642328206

This paper presents a portable optimization for MPI communications, called PRAcTICaL-MPI (Portable Adaptive Compression Library- MPI). PRAcTICaL-MPI reduces the data volume exchanged among processes by using lossless compression and offers two main advantages. Firstly, it is independent of the MPI implementation and the application used. Secondly, it allows for turning the compression on and off and selecting the most appropriate compression algorithm at runtime, depending on the characteristics of each message and on network performance. We have validated PRAcTICaL-MPI in different MPI implementations and HPC clusters. The evaluation shows that compressing MPI messages with the best algorithm and only when it is worthwhile, we obtain a great reduction in the overall execution time for many of the scenarios considered.

关键词： MPI Library parallel techniques High-Performance Computing Compression algorithms Adaptive systems Portable optimizations

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