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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是381-390 订阅

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PRIAG: Proximal Reweighted Incremental Aggregated Gradient Algorithm for Distributed Optimizations 20th

PRIAG: Proximal Reweighted Incremental Aggregated Gradient A...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Deng, Xiaoge Sun, Tao Liu, Feng Huang, Feng Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc PDL Changsha 410073 Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Large-scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e., algorithms that leverage multiple workers for training. However, collecting the information from all workers in every iteration is sometimes expensive or even prohibitive. In this paper, we propose an iterative algorithm called proximal reweighted incremental aggregated gradient (PRIAG) for solving a class of nonconvex and nonsmooth problems, which are ubiquitous in machine learning tasks and distributed optimization problems. In each iteration, this algorithm just needs the information from one worker due to the incremental aggregated method. Combined with the reweighted technique, we only require an easy-to-calculate proximal operator to deal with the nonconvex and nonsmooth properties. Using the Lyapunov function analysis method, we prove that the PRIAG algorithm is convergent under some mild assumptions. We apply this approach to nonconvex nonsmooth problems and distributed optimization tasks. Numerical experiments on both synthetic and real data sets show that our algorithm can achieve comparative learning performance, but more efficiently, compared with previous nonconvex solvers.

关键词： Distributed optimization Incremental method Proximal operator Nonconvex optimization Iteratively reweighted algorithm

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Machine Learning Migration for Efficient Near-Data processing

Machine Learning Migration for Efficient Near-Data Processin...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Aline S. Cordeiro Sairo R. dos Santos Francis B. Moreira Paulo C. Santos Luigi Carro Marco A. Z. Alves Federal University of Paraná Curitiba Brazil Federal Rural University of Semi-arid Angicos Brazil Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil

Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its high impact an time and energy consumption. In this context, the Near-Data processing (NDP) architectures emerged as a prominent solution to increasing data by drastically reducing the required amount of data movement For NDP, we see three main approaches, Application-Specific Integrated Circuits (ASICs), full Central processing Units (CPUs) and Graphics processing Units (GPIIs), or vector units integration. However, previous work considered only ASICs, CPUs and GPUs when executing ML algorithms inside the memory. In this paper, we present an approach to execute ML algorithms near-data, using a general-purpose vector architecture and applying near-data parallelism to kernels from KNN, MEP, and CNN algorithms. To facilitate this process, we also present an NDP intrinsics library to ease the evaluation and debugging tasks. Our results show speedups up to fox for KNN, 11× for MLP, and 3× for convolution when processing near-data compared to a high-performance ×86 baseline.

关键词： Integrated circuits Machine learning algorithms Graphics processing units Machine learning Computer architecture Tools parallel processing

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parallel Applications Mapping onto Heterogeneous MPSoCs Interconnected Using Network on Chip 1

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6th international conference on Mobile, Secure and Programmable Networking, MSPN 2020

作者： Belkacemi, Dihia Daoui, Mehammed Bouzefrane, Samia Laboratoire de Recherche en Informatique Université de Tizi-Ouzou Tizi Ouzou Algeria CEDRIC Lab CNAM Paris France

ISBN: (数字)9783030675509

ISBN: (纸本)9783030675493

To meet the growing requirements of today’s applications, multiprocessor architectures (MPSoCs) interconnected with a network on chip (NoC) are considered as a major solution for future powerful embedded systems. Mapping phase is one of the most critical challenge in designing these systems. It consists of assigning application’ tasks on the target platform which can have a considerable influence on the performance of the final system. Due to the large solutions’ research space generated by both the application complexity and the platforms, this mapping phase can no longer be done manually and hence it requires powerful exploration tools called DSE (Design Space Exploration Environment). this paper proposes a new tool for static mapping applications on NoC based on heterogeneous MPSoCs. this tool integrates several multiobjective optimization algorithms that can be specified in order to explore different solutions’ spaces, mainly: exact method, metaheuristics (population-based metaheuristics and single solution-based ones) as well as hybrid ones;it offers different cost functions (defined using analytical or simulation models). the user can specify them or define others easily and it provides an easy way to evaluate the performance of the Pareto front returned by different algorithms using multiple quality indicators. We also present a series of experiments by considering several scenarios and give guidelines to designers on choosing the appropriate algorithm based on the characteristics of the mapping problem considered. © 2021, Springer Nature Switzerland AG.

关键词： Network-on-chip

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DOS-GAN: A Distributed Over-Sampling Method Based on Generative Adversarial Networks for Distributed Class-Imbalance Learning 20th

DOS-GAN: A Distributed Over-Sampling Method Based on Generat...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Guan, Hongtao Ma, Xingkong Shen, Siqi Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to classimbalance situations. the imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. the imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.

关键词： Class-imbalance learning Distributed learning Generative adversarial networks Data isolated islands

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Proceedings of the 34th ACM international conference on Supercomputing, ICS 2020

Proceedings of the 34th ACM International Conference on Supe...

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34th ACM international conference on Supercomputing, ICS 2020

ISBN: (纸本)9781450379830

the proceedings contain 40 papers. the topics discussed include: a scalable framework for solving fractional diffusion equations;fast distributed bandits for online recommendation systems;wavefront parallelization of recurrent neural networks on multi-core architectures;NV-group: link-efficient reduction for distributed deep learning on modern dense GPU systems;a coordinate-oblivious index for high-dimensional distance similarity searches on the GPU;V-combiner: speeding-up iterative graph processing on a shared-memory platform with vertex merging;efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs;parallelizing pruned landmark labeling: dealing with dependencies in graph algorithms;Graptor: efficient pull and push style vectorized graph processing;and SB-fetch: synchronization aware hardware prefetching for chip multiprocessors.

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Accelerating Nested Data parallelism: Preserving Regularity 26th

Accelerating Nested Data Parallelism: Preserving Regularity

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26th international conference on parallel and Distributed Computing (Euro-Par)

作者： van den Haak, Lars B. McDonell, Trevor L. Keller, Gabriele K. de Wolff, Ivo Gabe Eindhoven Univ Technol Eindhoven Netherlands Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9783030576752;9783030576745

Irregular nested data-parallelism is a powerful programming model which enables the expression of a large class of parallel algorithms. However, it is notoriously difficult to compile such programs to efficient code for modern parallel architectures. Regular data-parallelism, on the other hand, is much easier to compile to efficient code, but too restricted to express some problems conveniently or in a manner to exploit the full parallelism. We extend the regular data-parallel programming model to allow for the parallel execution of array-level conditionals and iterations over irregular nested structures, and present two novel static analyses to optimise the code generated for these programs which reduces the costs of this more powerful irregular model. We present benchmarks to support our claim that these extensions are effective as well as feasible, as they enable to exploit the full parallelism of an important class of algorithms, and together with our optimisations lead to an improvement in absolute performance over an implementation limited to exploiting only regular parallelism.

关键词： Arrays GPGPU Nested data parallelism parallel functional programming

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Multi-scaled Non-local Means parallel Filters for Medical Image Denoising 20th

Multi-scaled Non-local Means Parallel Filters for Medical Im...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Rabbouch, Hana Ben Messaoud, Othman Saadaoui, Foued Fac Sci Lab LR18ES15 Algebre Theorie Nombres & Anal Nonli Monastir 5019 Tunisia Compagnie Phosphates Gafsa CPG Cite Bayech 2100 Gafsa Tunisia King Abdulaziz Univ POB 80203 Jeddah 21589 Saudi Arabia

ISBN: (纸本)9783030602451;9783030602444

In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. the extraordinary development of the denoising area is no doubt due to the ever expanding and successful computing technology, but also to the emergence of the multi-resolution analysis (MRA) on both mathematical and algorithmic levels. However, many denoising techniques still remain ineffective in dealing with certain types of noise. Other methods can be too expensive, given their nested and complicated structure. therefore, in this paper, A new multi-scale parallel denoising paradigm is defined and tested. A comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). the comparison is carried out in this framework of multi-scaled parallel denoising, where a Non-Local Means (NLM) filter is implemented and adjusted scale-by-scale to a sample of X-ray benchmark images. Some state-of-the-art denoising methods were also used in the evaluation. the numerical results proved the effectiveness of the multi-scaled parallel denoising in terms of accuracy and speed of convergence, especially when the NLM filtering is coupled with the EMD. this shows a bright future for their medical use in the next few years.

关键词： Medical imaging Non-local means Multi-scaled denoising Empirical mode decomposition Wavelets

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Evaluating the performance and improving the usability of parallel and distributed Word Embeddings tools 28

Evaluating the performance and improving the usability of pa...

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28th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： da Silva, Matheus L. Meyer, Vinicius Kirchoff, Dionatra F. Nero, Joaquim F. S. Vieira, Renata De Rose, Cesar A. F. Pontific Catholic Univ Rio Grande PUCKS Polytech Sch Ipiranga Ave 6681 BR-90619900 Porto Alegre RS Brazil

ISBN: (纸本)9781728165820

the representation of words by means of vectors, also called Word Embeddings (WE), has been receiving great attention from the Natural Language processing (NLP) field. WE models are able to express syntactic and semantic similarities, as well as relationships and contexts of words within a given corpus. Although the most popular implementations of WE algorithms present low scalability, there are new approaches that apply High-Performance Computing (HPC) techniques. this is an opportunity for an analysis of the main differences among the existing implementations, based on performance and scalability metrics. In this paper, we present a study which addresses resource utilization and performance aspects of known WE algorithms foetid in the literature. To improve scalability and usability we propose a wrapper library for local and remote execution environments that contains a set of optimizations such as the pWord2vec, pWord2vec_MPI, Wang2vec and the original Word2vec algorithm. Utilizing these optimizations it is possible to achieve an average performance gain of 15x for multicores and 105x for multinodes compared to the original version. there is also a big reduction in the memory footprint compared to the most popular python versions.

关键词： Natural Language processing Word2vec Performance Evaluation

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CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous parallel Variants 20th

CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochas...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Peng, Yaqiong Fei, Haiqiang Li, Lun Ding, Zhenquan Hao, Zhiyu Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. the unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. the experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

关键词： Machine learning Stochastic gradient descent Streaming data parallel algorithm Multicore

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A New DOA Estimation Algorithm Based on PSO-Gauss-Newton

A New DOA Estimation Algorithm Based on PSO-Gauss-Newton

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international conference on Information, Communication and Networks (ICICN)

作者： Xuerong Cui Rongrong Zhou Haihua Chen Yucheng Zhang Shibao Li Jingyao Zhang College of Oceanography and Space Informatics China University of Petroleum Qingdao China Chinese Academy of Sciences Institute of Computing Technology Beijing China Engineering Laboratory of Intelligent Agricultural Machinery Equipment Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665411578

Direction-of-Arrival (DOA) estimation is a basic and important problem in sensor array signal processing. In order to solve this problem, many algorithms have been proposed. Among them, the Stochastic Maximum Likelihood (SML) algorithm has become one of the most concerned algorithms because of its high DOA accuracy. However, the computational complexity of SML algorithm is very high, so Gauss-Newton algorithm is used as the analytical algorithm of SML in this paper. the traditional Gauss-Newton algorithm used in DOA estimation has some defects: (1) over reliance on the choice of initial values (2) fall into local optimum easily. In order to solve these defects and further reduce the computational complexity, this paper proposes a new DOA estimation algorithm based on PSO-Gauss-Newton. First of all, a limited solution space is proposed based on the precondition that the estimated signal must be non-negative definite. then, according to the idea of PSO(Particle Swarm Optimization) algorithm, multiple scattering points are randomly distributed in the limited solution space. Each initial particle performs Gauss-Newton algorithm iteration separately. Finally, the global optimal solution is determined by comparison of all the convergence values. Simulation results the computational complexity of this algorithm is almost comparable to that of MUSIC algorithm.

关键词： Maximum likelihood estimation Direction-of-arrival estimation Simulation Signal processing algorithms Scattering parallel processing Multiple signal classification

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