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检索条件"任意字段=6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP"

共 292 条记录，以下是61-70 订阅

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A Distributed Framework for Online Stream Data Clustering 20th

A Distributed Framework for Online Stream Data Clustering

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ding, Jiafeng Fang, Junhua Chao, Pingfu Xu, Jiajie Zhao, PengPeng Zhao, Lei Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9783030602451;9783030602444

the recent prevalence of positioning sensors and mobile devices generates a massive amount of spatial-temporal data from moving objects in real-time. As one of the fundamental processes in data analysis, the clustering on spatial-temporal data creates various applications, like event detection and travel pattern extraction. However, most of the existing works only focus on the offline scenario, which is not applicable to online time-sensitive applications due to their low efficiency and ignorance of temporal features. In this paper, we propose a distributed streaming framework for spatial-temporal data clustering, which accepts various clustering algorithms while ensuring low resource consumption and result correctness. the framework includes a dynamic partitioning strategy for continuous load-balancing and a cluster-merging algorithm based on convex hulls [10], which guarantees the result correctness. Extensive experiments on real dataset prove the effectiveness of our proposed framework and its advantage over existing solutions.

关键词： Real-time cluster analysis Distributed stream processing Spatial-temporal data mining Top-k query parallel computing

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RTEF-pp: A Robust Trust Evaluation Framework with Privacy Protection for Cloud Services Providers 19th

RTEF-PP: A Robust Trust Evaluation Framework with Privacy Pr...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhong, Hong Zou, JianZhong Cui, Jie Xu, Yan Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China Anhui Engn Lab IoT Secur Technol Hefei 230039 Peoples R China

ISBN: (纸本)9783030389918;9783030389901

the trust problem of Cloud Services Providers (CSPs) has become one of the most challenging issues for cloud computing. To build trust between Cloud Clients (CCs) and CSPs, a large number of trust evaluation frameworks have been proposed. Most of these trust evaluation frameworks collect and process evidence data such as the feedback and the preferences from CCs. However, evidence data may reveal the CCs' privacy. So far there are very few trust frameworks study on the privacy protection of CCs. In addition, when the number of malicious CCs' feedback increases, the accuracy of existing frameworks is greatly reduced. this paper proposes a robust trust evaluation framework RTEF-pp, which uses differential privacy to protect CCs' privacy. Furthermore, RTEF-pp uses the Euclidean distances between the monitored QoS values and CCs' feedback to detect malicious CCs' feedback ratings, and is not affected by the number of malicious CCs' feedback rating. Experimental results show that RTEF-pp is reliable and will not be affected by the number of malicious CCs' feedback rating.

关键词： Cloud computing Trust evaluation Robust Differential privacy

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parallel Belief Propagation Optimized by Coloring on GPUs 20th

Parallel Belief Propagation Optimized by Coloring on GPUs

引用

20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Hou, Junteng Si, Chengxiang Wang, Shupeng Wu, Guangjun Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Belief propagation (BP) is a message passing algorithm that infers over probabilistic graphical models. Its main computational workload, messages update, is suitable for GPU's massively parallel architecture. However, the efficiency of fully parallel BP is low, and traditional algorithms implemented on GPUs occupy amount of computing and memory resources. In this paper, we propose several GPU-friendly BP algorithms optimized by coloring. Color Wave (CW) algorithm performs multi-step coloring on residuals of non-convergent vertices to quickly obtain multiple disjoint partitions and vertices with the largest residuals in each partition, and then updates batches of messages in a fixed order. these operations are all suitable for parallelization and require little additional memory. To save time in each iteration, the Color Extract (CE) algorithm only update messages on edges with the largest residuals among all adjacent edges. the Random Drop (RD) algorithm steadily increases the convergence degree by progressively reducing the messages update ratio of non-convergent edges. the experiments on different GPUs show that our algorithms perform well throughout the calculation process. Compared with state-of-the-art algorithms, CW algorithm converged most of the messages in previous iterations. the convergence degree of CE is higher than all other algorithms in most calculation processes. RD converges fast and always has a high degree of convergence.

关键词： Belief propagation GPUs parallel optimization Coloring operations Progressively reducing update ratio

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Optimizing B⁺-Tree Searches on Coupled CPU-GPU architectures 20th

Optimizing B<SUP>+</SUP>-Tree Searches on Coupled CPU-GPU Ar...

引用

20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Huang, Han Luan, Hua Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the B+-tree is an important index in the fields of data warehousing and database management systems. With the development of new hardware technologies, the B+-tree needs to be revisited to fully take advantage of hardware resources. In this paper, we focus on optimization techniques to increase the searching performance of B+-trees on the coupled CPU-GPU architecture. First, we propose a hierarchical searching approach on the single coupled GPU to efficiently deal with leaf nodes of B+-trees. It adopts a flexible strategy to determine the number of work items in a work group to search one key in order to reduce irregular memory accesses and divergent branches in the work group. Second, we present a co-processing pipeline method on the coupled architecture. the CPU and the integrated GPU process the sorting and searching tasks simultaneously to hide sorting and partial searching latencies. A distribution model is designed to support the workload balance strategy based on real-time performance. Our performance study shows that the hierarchical searching scheme provides an improvement up to 36% on the GPU compared to the baseline algorithm with fixed number of work items and the co-processing pipeline method further increases the throughput by a factor of 1.8. To the best of our knowledge, this paper is the first study to consider both the CPU and the coupled GPU to optimize B+-trees searches.

关键词： B+-trees the coupled architecture Integrated GPU Co-processing

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Selective Velocity Distributed Indexing for Continuously Moving Objects Model 19th

Selective Velocity Distributed Indexing for Continuously Mov...

引用

19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Bareche, Imene Xia, Ying Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the widespread of GPS embedded devices has lead to a ubiquitous location dependent services, based on the generated real-time location data. this introduced the notion of continuous querying, and with the aid of advanced indexing techniques several complex query types could be supported. However the efficient querying and manipulation of such highly dynamic data is not trivial, processing factors of crucial importance should be carefully thought out such as accuracy and scalability. In this study we focus on Continuous KNN (CKNN) queries processing, one of the most well-know spatio-temporal queries over large scale of continuously moving objects. In this paper we provide an overview of CKNN queries and related challenges, as well as an outline of proposed works in the literature and their limitations, before getting to our contribution proposal. We propose a novel indexing approach model for CKNN querying, namely VS-TIMO. the proposed structure is based on a selective velocity partitioning method, since we have different objects with varying speeds. Our structure base unit is a comprised of a non overlapping R-tree and a two dimensions grid. In order to enhance performances, we design a compact multi-layer index structure on a distributed setting, and propose a CKNN search algorithm for accurate results using a candidate cells identification process. We provide a comprehensive vision of our indexing model and the adopted querying technique.

关键词： Continuous KNN querying Moving objects indexing Distributed spatio-temporal indices Velocity partitioning based index parallel processing

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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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A Communication-Avoiding Algorithm for Molecular Dynamics Simulation 19th

A Communication-Avoiding Algorithm for Molecular Dynamics Si...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Wang, Bei Chen, Yifeng Hou, Chaofeng Peking Univ EECS Beijing 100871 Peoples R China Chinese Acad Sci Inst Proc Engn Beijing Peoples R China

ISBN: (纸本)9783030389918;9783030389901

Molecular dynamics and many similar time-dependent computing tasks are defined as simple state updates over multiple time steps. In recent years, modern supercomputing clusters have enjoyed fast-growing compute capability and moderate-growing memory bandwidth, but their improvement of network bandwidth/latency is limited. In this paper, we propose a new communication-avoiding algorithmic model based on asynchronous communications which, unlike BSP, records and handles multiple iterative states together. the basic idea is to let computation run in small regular time steps while communications over longer dynamic time steps. Computation keeps checking inaccuracies so that the intervals between communications are small in volatile scenarios but longer when dynamics is smooth. this helps reduce the number of data exchanges via network communication and hence improve the overall performance when communication is the bottleneck. We test MD simulation of condensed covalent materials on the Sunway TaihuLight. For best time-to-solution, the general-purpose supercomputer Sunway TaihuLight performs 11.8K steps/s for a system with 2.1 million silicon atoms and 5.1 K steps/s for 50.4 million silicon atoms. this time-to-solution performance is close to those of state-of-art hardware solution. A software solution using general-purpose supercomputers makes the technology more accessible to the general scientific users.

关键词： BSP parallel-in-time Molecular dynamics

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Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs 19th

Rise the Momentum: A Method for Reducing the Training Error ...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Tang, Yu Yin, Lujia Zhang, Zhaoning Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. the experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.

关键词： Multiple GPUs Batchsize Distributed training Momentum

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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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ParaCA: A Speculative parallel Crawling Approach on Apache Spark 20th

ParaCA: A Speculative Parallel Crawling Approach on Apache S...

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Li, Yuxiang Zhang, Zhiyong Niu, DanMei Jing, Junchang Henan Univ Sci & Technol Luoyang 471023 Henan Peoples R China Henan Int Joint Lab Cyberspace Secur Applicat Luoyang 471023 Henan Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the World Wide Web today is growing at a phenomenal rate. the crawling approach is of vital importance to improve the efficiency of crawling the web. the existing sequent crawling algorithms are mostly time consuming and do not support large data well. In order to improve parallelism and efficiency of crawler on distributed network environments, based on the software thread-level speculation technique, this paper raises a speculative parallel crawler approach (ParaCA) on Apache Spark. By analyzing the process of web crawler, the ParaCA firstly hires a function to divide a crawling process into several sub-processes which can be implemented independently and then spawns a number of threads to speculatively crawl in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, ParaCA is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve a significant speedup improvement with compare to the traditional approach in average. In addition, with the growing number of working nodes, the execution time decreases gradually, and the speedup scales linearly. the results indicate that the crawling efficiency can be significantly enhanced by adopting this speculative parallel algorithm.

关键词： Crawling approach Speculative multithreading Apache Spark

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