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A fine-grain load-adaptive algorithm of the 2D discrete wavelet transform for multithreaded architectures

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2004年第1期64卷 68-78页

作者： Thulasiraman, P Khokhar, AA Heber, G Gao, GR Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada Univ Illinois Dept EECS Chicago IL 60607 USA Cornell Univ Ctr Theory Ithaca NY 14853 USA Univ Delaware Dept ECE Newark DE 19716 USA

In this paper we develop a load-adaptive multithreaded algorithm to compute 2D Discrete Wavelet Transform (DWT) and its implementation on a fine-grain multithreading platform. In a 2D DWT computation, the problem sizes reduces at every decomposition level and the length of the emerging computation paths also vary. The parallel algorithm proposed in this paper, dynamically scales itself to the varying problem size. During any iteration, the ratio of the number of local threads to the number of remote threads issued by a processor can be adjusted to be greater than I by controlling the algorithm parameters. This approach provides an opportunity to interleave computation and communication without explicitly introducing idle cycles on waiting for the remote threads to finish. Experimental results are reported based on the implementations of the proposed algorithm on a 20 node emulated multithreaded platform, EARTH-MANNA, specifically designed for fine-grain multithreaded paradigms. We show that multithreading implementations of the proposed algorithm are at least 2 times faster than the MPI-based message passing implementations reported in the literature, assuming the same processor speed. We further show that the proposed algorithm and implementations scale linearly with respect to problem and machine sizes. (C) 2003 Elsevier Inc. All rights reserved.

关键词： wavelet transform parallel algorithms image processing fine grain multithreading

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parallel algorithm for spin and spin-lattice dynamics simulations

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Physical Review E 2009年第4期79卷 046703-046703页

作者： Pui-Wai Ma C. H. Woo []Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong SAR China

To control numerical errors accumulated over tens of millions of time steps during the integration of a set of highly coupled equations of motion is not a trivial task. In this paper, we propose a parallel algorithm for spin dynamics and the newly developed spin-lattice dynamics simulation [P. W. Ma et al., Phys. Rev. B 78, 024434 (2008)]. The algorithm is successfully tested in both types of dynamic calculations involving a million spins. It shows good stability and numerical accuracy over millions of time steps (∼1 ns). The scheme is based on the second-order Suzuki-Trotter decomposition (STD). The usage can avoid numerical energy dissipation despite the trajectory and machine errors. The mathematical base of the symplecticity, for properly decomposed evolution operators, is presented. Due to the noncommutative nature of the spin in the present STD scheme, a unique parallel algorithm is needed. The efficiency and stability are tested. It can attain six to seven times speed up when eight threads are used. The run time per time step is linearly proportional to the system size.

关键词： parallel algorithms

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parallel and distributed paradigms for community detection in social networks: A methodological review

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期187卷 115956-115956页

作者： Naik, Debadatta Ramesh, Dharavath Gandomi, Amir H. Gorojanam, Naveen Babu Indian Inst Technol Indian Sch Mines Dept Comp Sci & Engn Dhanbad Bihar India Univ Technol Sydney Dept Engn & Informat Technol Ultimo NSW Australia

Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically,

关键词： Social Networks Community Detection parallel algorithms Distributed algorithms

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Concurrent, parallel Garbage Collection in Linear Time

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ACM SIGPLAN NOTICES 2014年第11期49卷 47-58页

作者： Brandt, Steven R. Krishnan, Hari Sharma, Gokarna Busch, Costas Louisiana State Univ Ctr Computat & Technol Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

This paper presents a new concurrent garbage collection algorithm based on two types of reference, strong and weak, to link the graph of objects. Strong references connect the roots to all the nodes in the graph but do not contain cycles. Weak references may, however, contain cycles. Advantages of this system include: (1) reduced processing, nontrivial garbage collection work is only required when the last strong reference is lost;(2) fewer memory traces to delete objects, a garbage cycle only needs to be traversed twice to be deleted;(3) fewer memory traces to retain objects, since the collector can often prove objects are reachable without fully tracing support cycles to which the objects belong;(4) concurrency, it can run in parallel with a live system without "stopping the world;" (5) parallel, because collection operations in different parts of the memory can proceed at the same time. Previous variants of this technique required exponential cleanup time [27, 31], but our algorithm is linear in total time, i.e. any changes in the graph take only O(N) time steps, where N is the number of edges in the affected subgraph (e.g. the subgraph whose strong support is affected by the operations).

关键词： algorithms Performance Experimentation Languages Design Termination Garbage Collection parallel programming theory and models Compilers and runtime systems Software for productivity parallel programming parallel algorithms Concurrent data structures

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AN EREW PRAM ALGORITHM FOR IMAGE COMPONENT LABELING

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1989年第3期11卷 258-262页

作者： CYPHER, R SANZ, JLC SNYDER, L IBM CORP ALMADEN RES CTRDEPT COMP SCISAN JOSECA 95114

An important midlevel task for computer vision is addressed. The problem consists of labeling connected components in N/sup 1/2/*N/sup 2/2/ binary images. This task can be solved with parallel computers by using a simple and novel algorithm. The parallel computing model used is a synchronous fine-grained shared-memory model where only one processor can read from or write to the same memory location at a given time. This model is known as the exclusive-read exclusive-write parallel RAM (EREW PRAM). Using this model, the algorithm presented has O(log N) complexity. The algorithm can run on parallel machines other than the EREW PRAM. In particular, it offers an optimal image component labeling algorithm for mesh-connected computers.

关键词： Phase change random access memory Labeling Read-write memory Concurrent computing Pixel Image processing Computer vision parallel algorithms Computer science parallel processing INSPEC: Controlled Indexing random-access storage computational complexity computer vision parallel algorithms INSPEC: Non-Controlled Indexing mesh-connected computers parallel algorithm parallel processing EREW PRAM algorithm image component labeling computer vision binary images synchronous fine-grained shared-memory model exclusive-read exclusive-write parallel RAM

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parallel Three-Dimensional Distance Transform for Railway Alignment Optimization Using OpenMP

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2020年第5期146卷

作者： Song, Taoran Pu, Hao Schonfeld, Paul Li, Wei Zhang, Hong Ren, Yuhan Wang, Jie Hu, Jianping Peng, Xianbao Cent South Univ Sch Civil Engn Natl Engn Lab High Speed Railway Construct Changsha 410075 Peoples R China Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA China Railway First Survey & Design Inst Grp Co L State Key Lab Rail Transit Engn Informatizat Xian 710043 Peoples R China China Railway Eryuan Engn Grp Co Ltd 3 Tongjin Rd Chengdu 610031 Peoples R China China Railway Siyuan Survey & Design Grp Co Ltd 745 Heping Ave Wuhan 430063 Peoples R China

Railway alignment optimization is a large-scale and time-consuming civil engineering problem. To solve it, a three-dimensional distance transform (3D-DT) algorithm, which is a variant of the three-dimensional Euclidean distance transform (3D-EDT), was previously designed. However, that algorithm is quite computationally intensive. In addition, the 3D-DT is inherently sequential, and it is thus challenging to parallelize. Thus, this study focuses on improving the sequential 3D-DT by transforming it into a parallel one. First, existing representative parallel EDT methods are reviewed and assessed. Then the railway alignment optimization model and the sequential 3D-DT are described. After that, critical execution properties of the 3D-DT that significantly influence its parallelization are explored in depth. Lastly, a novel so-called parallel linkage method is presented. This parallel implementation, which is developed using the OpenMP library, is highly effective and scalable by fully exploiting the parallelism of the algorithm. Using this parallel 3D-DT method, a large-scale, real-world railway case is tested and analyzed in detail. The outcomes verify that the proposed parallel method can accelerate the optimization process significantly without reducing the quality of computation results.

关键词： Alignment optimization parallel algorithms Three-dimensional distance transform OpenMP

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A Hybrid MPI-OpenMP parallel Algorithm for the Assessment of the Multifractal Spectrum of River Networks

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WATER 2021年第21期13卷 3122页

作者： Primavera, Leonardo Florio, Emilia Univ Calabria Dipartimento Fis Cubo 31-C I-87036 Arcavacata Di Rende Italy Univ Calabria Dipartimento Matemat & Informat Cubo 30-B I-87036 Arcavacata Di Rende Italy

The possibility to create a flood wave in a river network depends on the geometric properties of the river basin. Among the models that try to forecast the Instantaneous Unit Hydrograph (IUH) of rainfall precipitation, the so-called Multifractal Instantaneous Unit Hydrograph (MIUH) rather successfully connects the multifractal properties of the river basin to the observed IUH. Such properties can be assessed through different types of analysis (fixed-size algorithm, correlation integral, fixed-mass algorithm, sandbox algorithm, and so on). The fixed-mass algorithm is the one that produces the most precise estimate of the properties of the multifractal spectrum that are relevant for the MIUH model. However, a disadvantage of this method is that it requires very long computational times to produce the best possible results. In a previous work, we proposed a parallel version of the fixed-mass algorithm, which drastically reduced the computational times almost proportionally to the number of Central Processing Unit (CPU) cores available on the computational machine by using the Message Passing Interface (MPI), which is a standard for distributed memory clusters. In the present work, we further improved the code in order to include the use of the Open Multi-Processing (OpenMP) paradigm to facilitate the execution and improve the computational speed-up on single processor, multi-core workstations, which are much more common than multi-node clusters. Moreover, the assessment of the multifractal spectrum has also been improved through a direct computation method. Currently, to the best of our knowledge, this code represents the state-of-the-art for a fast evaluation of the multifractal properties of a river basin, and it opens up a new scenario for an effective flood forecast in reasonable computational times.

关键词： river networks multifractal measures fixed-mass algorithm parallel algorithms

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Markov random field image segmentation using cellular neural network

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS 1997年第1期44卷 86-89页

作者： Sziranyi, T Zerubia, J Analogical and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (MTA-SZTAKI) Budapest Hungary I.N.R.I.A. Sophia Antipolis Sophia-Antipolis France

Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. with the Cellular Neural Networks (CNN), a new image processing toot is coming into consideration, Its VLSI implementation takes place on a single analog chip containing several thousands of cells. Herein se use the CNN UM architecture for statistical image segmentation. The Modified Metropolis Dynamics (MMD) method can be implemented into the raw analog architecture of the CNN, We are able to implement a (pseudo) random held generator using one layer (one memory/cell) of the CNN. We can introduce the whole pseudostochastic segmentation process in the CNN architecture using 8 memories/cell, We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw, With this architecture, a real VLSI CNN chip can execute a pseudostochastic relaxation algorithm of about 100 iterations in about 1 ms, In the proposed solution the segmentation is unsupervised. We have developed a pixel-level statistical estimation model. The CNN turns the original image into a smooth one. Then we have two gray-level values for every pixel: the original and the smoothed one. These two values are used for estimating the probability distribution of region label at a given pixel. Using the conventional first-order Markov Random Field (MRF) model, some misclassification errors remained at the region boundaries, because of the estimation difficulties in case of low SNR. By using a greater neighborhood, this problem has been avoided. In our CNN experiments, we used a simulation system with a fixed-point integer precision of 16 bits, Our results show that even in the case of the very constrained conditions of value-representations (the interval is (-64, +64), the accuracy is 0.002) can result in an effective and acceptable segmentation.

关键词： Markov processes VLSI analogue integrated circuits analogue processing circuits cellular neural nets estimation theory image segmentation neural chips neural net architecture parallel algorithms probability CNN UM architecture Markov random field image s parallel algorithms Cellular neural networks Neural network hardware Probability estimation theory image segmentation neural net architecture Analog integrated circuits analogue processing circuits Very large scale integration Markov random fields Markov chain

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AN IMPROVED parallel THINNING ALGORITHM

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COMMUNICATIONS OF THE ACM 1987年第2期30卷 156-160页

作者： HOLT, CM STEWART, A CLINT, M PERROTT, RH Queen's Univ. of Belfast Belfast Northern Ireland Queen's Univ. of Belfast Belfast Northern Ireland Queen's Univ. of Belfast Belfast Northern Ireland Queen's Univ. of Belfast Belfast Northern Ireland

Examines the use of an iterative parallel thinning algorithm in reducing a two-dimensional pattern of strokes to its skeleton by removing layers of edge elements until each stroke has unit thickness. Independent calculation of values for each iteration; Values of the eight neighbors of a central element; Constraints that a parallel algorithms need to satisfy.

关键词： parallel algorithms ITERATIVE methods (Mathematics)

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Demystifying parallel and Distributed Deep Learning: An In-depth Concurrency Analysis

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ACM COMPUTING SURVEYS 2019年第4期52卷 65-65页

作者： Ben-Nun, Tal Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci ETH Zurich Univ Str 6 CH-8092 Zurich Switzerland

Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.

关键词： Deep learning distributed computing parallel algorithms

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