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Classification Framework for the parallel Hash Join with a Performance Analysis on the GPU 15

Classification Framework for the Parallel Hash Join with a P...

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Wozniak, Kinga Anna Schikuta, Erich Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9781538637906

The hash join operator is one of the most important relational operators in database applications and a prominent research topic in the domain of parallel processing. However, up to date, no consistent algorithm design guidelines for high-performance implementations on parallel platforms have been derived from the available experimental results. In this work we define a taxonomy of the parallel hash join operator landscape and categorize state of the art research accordingly. Moreover, we implement and benchmark three taxonomy types: A sequential implementation on the CPU, a hybrid CPU-GPU implementation as well as a fully parallel version on the GPU. The results show that (1) the hybrid CPU-GPU type outperforms the other two, showcasing the benefits of a good fit between algorithm type and hardware platform choice, (2) the poor end-to-end performance of the GPU-only type highlights the impact of GPU specific synchronization and contention issues that appear with an unfit design choice, (3) parallelization improves runtime by a factor of 2.2X in the end-to-end algorithm, a factor of 83X in the join phase and shows good scaling behavior with increasing number of threads. This proves that the GPU is a valuable co-processor option for computation offloading in database applications. We anticipate this classification framework to be a starting-point for design decisions for parallel big data hash join operators on other heterogeneous systems.

关键词： relational database hash join parallel processing taxonomy graphics processor performance

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Misleading generalized itemset mining in the cloud 12

Misleading generalized itemset mining in the cloud

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12th IEEE international symposium on parallel and distributed processing with applications (ispa)

作者： Baralis, Elena Cagliero, Luca Cerquitelli, Tania Chiusano, Silvia Garza, Paolo Grimaudo, Luigi Pulvirenti, Fabio Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

ISBN: (纸本)9781479942930

In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributed computing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.

关键词： Generalized itemset mining distributed computing model cloud-based service smart city

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Optimization of key and value storage in vector map rendering 19

Optimization of key and value storage in vector map renderin...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Jing, Feng Jiang, Ying Chen, Shengbin Lv, Xirong He, Jiayi State Grid Shanxi Elect Power Co Taiyuan Peoples R China Xiamen Great Power Geo Informat Technol Co Ltd Xiamen Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665435741

With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. The introduction of distributed "cloud computing" technology has continuously improved the storage and management of massive data. Supported by a large number of distributed systems, it provides normal services to the outside world, as well as fast data location services. The system has good performance in data storage efficiency, node load balancing and system scalability, and has an efficient data positioning mechanism. The basic idea of the system design draws on the hash algorithm and the consistent hash algorithm, and improves the scalability of the basic algorithm. Through the three-tier layout: front-end server layer, consistent hash layer, data node layer, to maximize the consistency, availability, efficiency and scalability of the system.

关键词： distributed storage system consistent hash key value

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A Memory Saving Mechanism Based on Data Transferring for Pipeline parallelism 19

A Memory Saving Mechanism Based on Data Transferring for Pip...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Jiang, Wei Xu, Rui Ma, Sheng Wang, Qiong Hou, Xiang Lu, Hongyi Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Coll Comp Changsha Peoples R China

ISBN: (纸本)9781665435741

The correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. The multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. The memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.

关键词： multi-accelerator parallelism pipeline parallel scheme data transferring memory usage

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Research on CPN Model Reduction Focus On parallel Tested Behaviors 15

Research on CPN Model Reduction Focus On Parallel Tested Beh...

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Sun Tao Zhang Wei Guo Xin Wan Xiaoyun Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781538637906

Nowadays parallel software system is very common and practical. However, it is difficult to test parallel software, because the state space of parallel software is very large. Therefore, a parallel model simplification method based on CPN (Color Petri Net) is proposed. Based on the original CPN, the CPN model for the tested behavior(Tested Behavior of CPN, TBoCPN) is proposed. The target of the test is described as the tested behavior. The relevant behavior is described as the behavior related to the tested behavior, then, the homogeneous concurrent branch group and the selection branch set are divided. Finally, the branches of the concurrent branch group and the selected branch set, which satisfied the condition of algorithm, are sequentially processed by the inhibitor arcs. The experiment shows that the reduction rate is at least 60%, and before and after the reduction, the full coverage test path generated by the tested behavior is not affected, thus proving that the method is an effective test method.

关键词： parallel software CPN tested behavior related behavior concurrent branch group selection branch set full coverage

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parallel Symbolic Observation Graph 15

Parallel Symbolic Observation Graph

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Ouni, Hiba Klai, Kais Abid, Chiheb Ameur Zouari, Belhassen Univ Tunis El Manar Fac Sci Tunis Tunis 2092 Tunisia Univ Carthage Higher Sch Commun Tunis Mediatron Lab Tunis Tunisia Univ Paris 13 Sorbonne Paris Cite CNRS UMR 7030 LIPN Villetaneuse France

ISBN: (纸本)9781538637906

Model checking is a powerful technique for verifying and analyzing complex systems in many application fields. The analysis process of complex and concurrent systems often requires large computation resources which represents a real challenge. Even with simple configurations, the well-known state explosion problem is faced as the generated state space of such systems grows exponentially with the number of the system components. Numerous methods and techniques have been developed to overcome this problem including parallel and distributed-memory processing. In this paper, we aim at improving the performances of the so called Symbolic Observation Graph (SOG) construction by using parallelization techniques. A SOG is a hybrid structure where the transitions of a system are divided into observed and unobserved ones. The nodes of this graph are then defined as sets of states linked with unobserved transitions (and encoded symbolically with a BDD) and edges are labeled with observed transitions only (and are explicitly represented). We propose two parallel algorithms to build the SOG. The first algorithm is dedicated for shared memory architectures, and is based on the distribution of the SOG construction on several threads using a dynamic load balancing scheme. The second algorithm is proposed for distributed memory architectures, and distributes the SOG construction on processes using a static load balancing scheme. These two algorithms are implemented and their performances are studied and compared to each other and to the sequential construction of the SOG.

关键词： distributed memory Multi threading parallel model checking Symbolic reachability

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parallel Computing in DNNs Using CPU and MIC 15

Parallel Computing in DNNs Using CPU and MIC

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Fan, Sijiang Zhang, Shiqing He, Ximing Fei, Jiawei Shen, Li Wang, Zhiying Natl Univ Def Technol Sch Comp State Key Lab High Performance Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538637906

Acceleration for the training process of Deep Neural Networks (DNNs) has been the focus of deep learning field. There were many researches of accelerating deep learning on different platforms. Among them, Intel Xeon Phi Co-processor is a many-core platform which provides both strong programmability and high performance. But previous work about Intel Many Integrated Core (MIC) focused on parallel computing only in MIC. In this paper, we speed up the training process of DNNs applied for automatic speech recognition with CPU+MIC architecture. In this architecture, the training process of DNNs is executed both on MIC and CPU. We apply several optimization methods for I/O and calculation and set up experiments to approve these methods. Putting all methods together, results show that our optimized algorithm acquires about 20x speedup compared with the original sequential algorithm on CPU which uses one core.

关键词： DNNs Hybrid Architecture parallel Computing Load Balance

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parallel Counterfactual Regret Minimization in Crowdsourcing Imperfect-information Expanded Game 19

Parallel Counterfactual Regret Minimization in Crowdsourcing...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Zhang, Jie Li, Kefan Zhang, Baoming Xu, Ming Wang, Chongjun Nanjing Univ State Key Lab Novel Software Technol Dept Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665435741

Counterfactual regret minimization (CFR) is one of the most widely used algorithms in iterative optimization algorithms. It is used to solve complex imperfect-information game problems. This paper introduced the Global Counterfactual Regret Minimization Local Update (GCFR+) to solve task planning problems in a crowdsourcing environment. We designed a parallel mechanism to alleviate possible parallel conflicts in actual crowdsourcing scenarios and increase personal rewards. First of all, we chose to test the performance of GCFR+ on data sets with different scales. Then we compared the result with the result of the decision model with a parallel mechanism. It can be seen that the parallel mechanism has significantly improved the efficiency of the decision model. Finally, unlike general CFR, we proved that GCFR+ is applicable to decision tree pruning of imperfect-information games.

关键词： Crowdsourcing task planning Counterfactual regret minimization parallel mechanisms dynamic gaming

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Parameter Servers Placement for distributed Deep Learning in Edge computing 19

Parameter Servers Placement for Distributed Deep Learning in...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ispa)

作者： Yan, Jiaquan Wu, Yalan Wu, Jigang Chen, Long Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9781665435741

The architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. Therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. Then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Edge Computing distributed Deep Learning Parameter Server Placement

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parallel Skyline Computation for Partially Ordered Domains 15

Parallel Skyline Computation for Partially Ordered Domains

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Yin, Bo Gu, Ke Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538637906

The skyline queries help users handle the huge amount of available data by finding a set of interesting points. As the dataset sizes are constantly increasing and skyline queries are computationally expensive, it is critical to compute such queries by utilizing parallelism. Existing works deal exclusively with the totally ordered attribute domains. In this paper, we present a framework, named PSLP, for parallel skyline evaluation for data with both totally and partially ordered domains. We introduce a new partial-to-order mapping scheme that guarantees the correctness of the mapping by preserving incomparability and preference with low mapping cost. We also propose a novel logical partitioning for parallel processing where data space are partitioned according to their incomparability and preference relationships by using a pivot point. The logical partitioning can prune away partitions that do not contain any skyline point at the partitioning processing. An extensive performance evaluation confirms the efficiency and effectiveness of the proposed approach.

关键词： skyline queries partially-ordered domain parallel computation data partitioning

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