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asynchronous parallelization of particle swarm optimization through digital pheromone sharing

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2009年第3期39卷 263-281页

作者： Kalivarapu, Vijay K. Winer, Eliot H. Iowa State Univ Dept Mech Engn Virtual Real Applicat Ctr Ames IA 50011 USA

In this paper, a model for sharing digital pheromones between multiple particle swarms to search n-dimensional design spaces in an asynchronous parallel computing environment is presented. Particle swarm optimization (PSO) is an evolutionary technique used to effectively search multi-modal design spaces. With the aid of digital pheromones, members in a swarm can better communicate with each other to improve search performance. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching the global optimum in both single and coarse grain synchronous parallel computing environments. In the coarse grain approach, multiple swarms are simultaneously deployed across various processors and synchronization is carried out only when all swarms achieved convergence, in an effort to reduce processor-to-processor communication and network latencies. However, it is theorized that with an appropriate parallelization scheme, the benefits of digital pheromones and communication between swarms can outweigh the network bandwidth latencies resulting in improved search efficiency and accuracy. To explore this idea, a swarm is deployed in the design space across different processors. Through an additional processor, each part of the swarm can communicate with the others. While digital pheromones aid communication within a swarm, the developed parallelization model facilitates communication between multiple swarms resulting in improved search accuracy and efficiency. The development of this method and results from solving several multi-modal test problems are presented.

关键词： Particle swarm optimization Digital pheromones asynchronous parallelization parallelization

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FPGA acceleration by asynchronous parallelization for simultaneous image reconstruction and segmentation based on the Mumford-Shah regularization 8

FPGA acceleration by asynchronous parallelization for simult...

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Conference on Image Reconstruction from Incomplete Data VIII

作者： Zhang, Wentai Luo, Guojie Shen, Li Page, Thomas Li, Peng Jiang, Ming Maass, Peter Cong, Jason Peking Univ Beijing Peoples R China PKU UCLA Joint Res Inst Sci & Engn Los Angeles CA USA NUDT Collaborat Innovat Ctr High Performance Comp Changsha Hunan Peoples R China Univ Bremen D-28359 Bremen Germany Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781628417661

X-ray computed tomography (CT) is an important technique for noninvasive clinical diagnosis and nondestructive testing. In many applications a number of image processing steps are needed before the image features are available. One of these processing steps is image segmentation, which generates the edge and the structural features of the regions of interest. The conventional flow is to first reconstruct images and then apply image segmentation methods on reconstructed images. In contrast, an emerging technique obtains the tomographic image and segmentation simultaneously, which is especially useful in the case of limited data. An iterative method for simultaneous reconstruction and segmentation (SRS) with Mumford-Shah model has been proposed, which not only regularizes the ill-posed tomographic reconstruction problem, but also produces the image segmentation at the same time. The Mumford-Shah model is both mathematically and computationally chanllenging. In this paper, we propose an asynchronous ray-parallel algorithm of the SRS method and accelerate it using field-programmable gate array (FPGA) devices, which drastically improves the energy efficiency. Experimental results show that the FPGA implementation achieves a 1.2x speedup with an energy efficiency as great as 58x, over the GPU implementation.

关键词： image reconstruction image segmentation Mumford-Shah regularization gamma-convergence asynchronous parallelization FPGA acceleration hardware pipelining

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asynchronous Background Processing for accelerated simulation of wireless communication on multi-core systems

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COMPUTER COMMUNICATIONS 2022年 193卷 396-409页

作者： Buse, Dominik S. Echterling, Georg Dressler, Falko Paderborn Univ Dept Comp Sci Paderborn Germany TU Berlin Sch Elect Engn & Comp Sci Berlin Germany

Discrete event simulation (DES) is an important tool for the development and analysis of wireless networks. However, with increasing network size and complexity, the computational effort and simulation time increases significantly, often exponentially. This increase in response time may be critical if DES is interfacing real-time systems like Hardware in the Loop (HIL) or network emulation. It also slows down development cycles of users designing or debugging simulation models. Most popular DES software packages run single-threaded. Thus, they achieve only limited performance improvements from more modern multi-core CPUs. At the same time, existing approaches for parallel simulation of networks do not perform well on wireless systems or require complex paradigm shifts in simulation models. In this paper, we propose asynchronous Background Processing (ABP) to accelerate the simulation of wireless communication on multi-core systems. By moving expensive computation from the main thread into asynchronous tasks computed by background threads, it accelerates the progression of events and thus reduces response time. Tasks are started as early as possible to exploit the time the main thread spends processing other events, ideally providing results before they are needed in the simulation. We showcase the application of ABP using Veins, a popular vehicular network simulator, demonstrating speedups of up to 3.5 on typical desktop platforms. We further perform an in-depth analysis using advanced profiling techniques to investigate the effectiveness of the parallelization and guide further optimizations.

关键词： Parallel simulation Wireless network simulation asynchronous parallelization Vehicular networking

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A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems

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APPLIED MATHEMATICAL MODELLING 2015年第14期39卷 4223-4241页

作者： Moraes, Antonio O. S. Mitre, Joao F. Lage, Paulo L. C. Secchi, Argimiro R. Univ Fed Rio de Janeiro COPPE Programa Engn Quim BR-21941972 Rio De Janeiro RJ Brazil

The application of the Particle Swarm Optimization (PSO) method to large engineering problems is strongly limited by the required computational cost. This limitation comes from the large number of particles needed to optimize the many-variable function, the high computational cost of its evaluation and the lack of an adequate criteria to early detect the approach of the global optimum. The first two cost sources can be mitigated by an efficient parallel implementation of the PSO method but the last one need the development of a robust convergence criterion for the algorithm. This work develops an efficient and robust optimization method by using a new convergence criterion in an asynchronous parallel implementation of PSO. In the optimization of benchmark test functions, this method showed very good performance, with parallel efficiency between 80% and 100%, and excellent robustness, always detecting the global optimum. Finally, the method was successfully applied to an actual estimation problem with 81 parameters. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Optimization Particle swarm MPI asynchronous parallelization Parameter estimation Convergence criterion

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An asynchronous parallel MLFMA for scattering at multiple dielectric objects

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2008年第8期56卷 2346-2355页

作者： Fostier, Jan Olyslager, Femke Univ Ghent Dept Informat Technol INTEC B-9000 Ghent Belgium

In this paper, a new strategy for the parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed memory computers is presented. By using an asynchronous implementation of the parallel MLFMA, an efficient parallelization scheme is obtained when multiple dielectric objects are involved in the simulation. Furthermore, a better spreading of the communication through time is obtained, avoiding both communication in bursts and synchronization at each MLFMA level. This proves especially beneficial when slower interconnection networks are used.

关键词： asynchronous parallelization electromagnetic scattering multilevel fast multipole algorithm (MLFMA)

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Optimizing Multi-Grid Preconditioned Conjugate Gradient Method on Multi-Cores

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第5期35卷 768-779页

作者： Yuan, Fan Yang, Xiaojian Li, Shengguo Dong, Dezun Huang, Chun Wang, Zheng Natl Univ Def Technol Dept Math Changsha 410073 Peoples R China Xiangtan Univ Dept Math Xiangtan 411105 Peoples R China Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

Multigrid preconditioned conjugate gradient (MGPCG) is commonly used in high-performance computing (HPC) workloads. However, MGPCG is notoriously challenging to optimize since most of its computation kernels are memory-bounded with low arithmetic intensity and non-trivial communication patterns among parallel processes. This article presents new techniques to improve the data locality and reduce the communication overhead of MGPCG by first merging the kernels of multigrid (MG). We then develop an asynchronous neighboring communication algorithm to reduce the data communications across parallel processes. We demonstrated the benefits of our approach by applying it to the high-performance conjugate gradient (HPCG) benchmark and integrating it with a real-life algebraic multigrid package. We test the resulting software implementations on three ARMv8 and one Intel Xeon system. Experimental results show that our approach leads to a 1.62x-2.54x speedup over the engineer- and vendor-tuned HPCG implementations across various workloads and platforms.

关键词： Multigrid HPCG symmetric Gauss-Seidel asynchronous parallelization

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asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第2期32卷 445-459页

作者： Qin, Wen Luo, Xin Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

A latent feature analysis (LFA) model is very efficient in representation learning to high-dimensional incomplete (HDI) data emerging from various Big Data-based applications. The stochastic gradient descent (SGD) is frequently adopted as the learning algorithm by an LFA model owing to its ease of implementation. However, the standard SGD-based LFA model's training process is serial, which greatly decreases its scalability on massive HDI data. On the other hand, all existing parallel SGD-based LFA models suffer from low speedup due to their frequent synchronizations during the training process. Motivated by this discovery, this article proposes an asynchronous parallel fuzzy stochastic gradient descent (APF-SGD) algorithm to establish an efficiently parallelized LFA model on shared memory with threefold ideas: first, decoupling the update interdependences among heterogeneous latent features alternatively, thereby achieving two parallelizable subtasks without learning information loss;second, proposing a novel parallelized learning scheme that eliminates the synchronizations from both the subtask and thread perspectives, i.e., subtasks and their affiliated threads are taken simultaneously and continuously without any synchronization;third, applying the fuzzy optimization to its hyperparameters, thus enabling its adaptation in training. Theoretical convergence proof illustrates that the newly proposed APF-SGD algorithm guarantees the convergence of a resultant LFA model. Experimental results on four real HDI datasets illustrate that an APF-SGD-based LFA model outperforms several state-of-the-art parallel SGD-based LFA models in both missing data estimation accuracy and parallelization speedup.

关键词： Training Representation learning Data models Stochastic processes Computational modeling Analytical models Proteins asynchronous parallelization fuzzy optimization high-dimensional incomplete (HDI) data latent feature analysis (LFA) shared-memory stochastic gradient descent (SGD)

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On parallel policies for ranking and selection problems

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JOURNAL OF APPLIED STATISTICS 2018年第9期45卷 1690-1713页

作者： Kaminski, Bogumil Szufel, Przemyslaw Warsaw Sch Econ Decis Anal & Support Unit Al Niepodleglosci 162 Warsaw Poland

In this paper we develop and test experimental methodologies for selection of the best alternative among a discrete number of available treatments. We consider a scenario where a researcher sequentially decides which treatments are assigned to experimental units. This problem is particularly challenging if a single measurement of the response to a treatment is time-consuming and there is a limited time for experimentation. This time can be decreased if it is possible to perform measurements in parallel. In this work we propose and discuss asynchronous extensions of two well-known Ranking & Selection policies, namely, Optimal Computing Budget Allocation (OCBA) and Knowledge Gradient (KG) policy. Our extensions (asynchronous Optimal Computing Budget Allocation (AOCBA) and asynchronous Knowledge Gradient (AKG), respectively) allow for parallel asynchronous allocation of measurements. Additionally, since the standard KG method is sequential (it can only allocate one experiment at a time) we propose a parallel synchronous extension of KG policy - Synchronous Knowledge Gradient (SKG). Computer simulations of our algorithms indicate that our parallel KG-based policies (AKG, SKG) outperform the standard OCBA method as well as AOCBA, if the number of evaluated alternatives is small or the computing/experimental budget is limited. For experimentations with large budgets and big sets of alternatives, both the OCBA and AOCBA policies are more efficient.

关键词： Knowledge gradient stochastic optimization asynchronous parallelization synchronous paralellization Bayes procedures

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asynchronous parallel Potts model for simulation of grain growth

Asynchronous parallel Potts model for simulation of grain gr...

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Symposium on Modeling, Control and Optimizatin in Ferrous and Nonferrous Industry held at the Materials Science and Technology Conference

作者： Manohar, PA Rollett, AD Carnegie Mellon Univ Dept Mat Sci & Engn Pittsburgh PA 15213 USA

ISBN: (纸本)0873395611

This paper describes an implementation of the Potts model for simulation of grain growth in 2D on a distributed-memory parallel architecture. The simulation scheme utilizes a continuous-time, asynchronous parallel version of the n-fold way algorithm. Each Processing Element (PE) carries a piece of the global simulation domain while the virtual topology of the PEs is in the form of a square grid in 2D and Torus in 3D. Different PEs have different local simulated times. Inter-processor communication is carried by the Message Passing Interface (MPI) routines. Sample results for grain growth simulation in 2D are presented for a 840 x 840 global domain size distributed over 16 PEs. The grain growth kinetics obtained in the simulation of pure, isotropic systems is in good agreement with the theory. Grain size distributions obtained in the parallel simulation are well described by the lognormal distribution, thus validating the simulation procedure.

关键词： metropolis algorithm n-fold way algorithm asynchronous parallelization grain growth

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Optimizing Multi-grid Computation and parallelization on Multi-cores 23

Optimizing Multi-grid Computation and Parallelization on Mul...

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37th ACM International Conference on Supercomputing (ACM ICS)

作者： Yang, Xiaojian Li, Shengguo Yuan, Fan Dong, Dezun Huang, Chun Wang, Zheng Natl Univ Def Technol Changsha Peoples R China Xiangtan Univ Xiangtan Peoples R China Univ Leeds Leeds W Yorkshire England

ISBN: (纸本)9798400700569

Multigrid algorithms are widely used to solve large-scale sparse linear systems, which is essential for many high-performance workloads. The symmetric Gauss-Seidel (SYMGS) method is often responsible for the performance bottleneck of MG. This paper presents new methods to parallelize and enhance the computation and parallelization efficiency of the SYMGS and MG algorithms on multi-core CPUs. Our solution employs a matrix splitting strategy and a revised computation formula to decrease the computation operations and memory accesses in SYMGS. With this new SYMGS strategy, we can then merge the two most time-consuming components of MG. On top of these, we propose a new asynchronous parallelization scheme to reduce the synchronization overhead when parallelizing SYMGS. We demonstrate the benefit of our techniques by integrating them with the HPCG benchmark and two real-life applications. Evaluation conducted on four architectures, including three ARMv8 and one x86, shows that our techniques greatly surpass the performance of engineer- and vendor-tuned implementations across various workloads and platforms.

关键词： Multigrid symmetric Gauss-Seidel asynchronous parallelization

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