2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)最新文献_第8页

I/O Scheduling for Limited-Size Burst-Buffers Deployed High Performance Computing 有限大小突发缓冲区的I/O调度部署高性能计算

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00021

Benbo Zha, Hong Shen

{"title":"I/O Scheduling for Limited-Size Burst-Buffers Deployed High Performance Computing","authors":"Benbo Zha, Hong Shen","doi":"10.1109/PDCAT46702.2019.00021","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00021","url":null,"abstract":"Burst-Buffers is a high throughput, small size intermediate storage system integrated between computing nodes and permanent storage system to mitigate the I/O bottleneck problem in modern High Performance Computing (HPC) platforms. This system, however, is unable to effectively handle variable-intensity I/O bursts resulted by unpredictable concurrent accesses to the shared Parallel File System (PFS). In this paper, we introduce a probabilistic I/O scheduling method that takes into account of the burst-buffer load state and instantaneous I/O load distribution of the system based on the probabilistic model of applications to relieve the I/O congestion when I/O load exceeds the PFS bandwidth caused by dynamic application interference. The proposed scheduling method for limited-size Burst-Buffers deployed HPC platforms makes online decision of probabilistic selection of concurrent I/O requests for going through (to PFS), buffering (to Burst-Buffers) or declination in accordance to both the available I/O bandwidth and the current buffer state in order to maximize system efficiency or minimize application dilation. Extensive experiment results on actual characteristic synthetic data show that our method handles the I/O congestion effectively.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114366970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly 在Hadoop中寻求作业并行性和吞吐量之间的权衡:一种动态参数调优的随机学习方法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00086

Ramesh Pokhrel, A. Rauniyar, A. Yazidi

{"title":"In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly","authors":"Ramesh Pokhrel, A. Rauniyar, A. Yazidi","doi":"10.1109/PDCAT46702.2019.00086","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00086","url":null,"abstract":"With the emergence of the concept of big data, Hadoop MapReduce has been the de facto standard programming model for processing a large amount of data stored on the different cluster nodes in a distributed manner. It is known that the implementation of MapReduce operation with the default configuration yields a low number of parallel running jobs. In fact, poor resource utilization and overall low performance are usually induced by the default configuration. Although a myriad of works has been carried out in the literature for optimally configuring Hadoop MapReduce, the absolute vast majority of those works only consider offline and static configuration. Those approaches are clearly ineffective as the load might change during execution requiring tuning again the configuration parameters. In this work, we rather focus on dynamical and adaptively configuring Hadoop MapReduce by changing the system level Maximum Application Master Resource in Percent (MARP) parameter on the fly. We show that adaptively tuning the MARP parameter yields a good trade-off between job parallelism and throughput. To achieve this, an optimal design which we call Adaptive Parameter Tuning of Hadoop (APTH) based on a novel variant of the Tsetlin Automata is devised. Comprehensive experimental results show that the resources are optimally and appropriately utilized, resulting in better job parallelism and throughput. Furthermore, it is found that our APTH approach spends 47% less time for job execution as compared to the default configuration.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"88 36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130795983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating Alignment for Short Reads Allowing Insertion of Gaps on Multi-Core Cluster 加速对准短读允许插入间隙在多核集群

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00019

Yongjie Yang, Cheng Zhong, Danyang Chen

引用次数: 0

A Scalable Parallel Computing Framework for Large-Scale Astrophysical Fluid Dynamics Numerical Simulation 大型天体物理流体动力学数值模拟的可扩展并行计算框架

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00066

I. Kulikov, I. Chernykh, A. Tchernykh

{"title":"A Scalable Parallel Computing Framework for Large-Scale Astrophysical Fluid Dynamics Numerical Simulation","authors":"I. Kulikov, I. Chernykh, A. Tchernykh","doi":"10.1109/PDCAT46702.2019.00066","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00066","url":null,"abstract":"The numerical simulation of complex astrophysical problems requires high-performance computing due to the large size of the problems and variety of simulated physical processes. In this paper, we present a new framework for the numerical simulation of astrophysical fluid dynamics. It is based on the mechanisms of combining distributed and parallel computing techniques, advanced vectorization for KNL, and Skylake-SP CPU architectures. Our new HydroBox3D framework uses large 3D meshes to solve problems such as the dynamics of stars or galaxies. In our framework, we use computational nodes with a large amount of memory (RAM or Intel Optane in memory mode) for mesh processing and typical computational nodes for the numerical simulation of astrophysical problems. We use MPI both for send/receive operations between computational nodes and for sending processed data for calculations from data nodes. For optimization of calculations, memory, and CPU usage, we use data vectorization, FMA3, and AVX-512 instructions for Intel Xeon Phi 72XX and Intel Xeon Scalable processors. Benchmark results on different CPU and MIC devices show the effectiveness of the proposed solution.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134533019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Protecting Sensitive Location Visits Against Inference Attacks in Trajectory Publishing 轨迹发布中保护敏感位置访问免受推理攻击

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00051

Xiangyu Liu, Manish Singh, Xiufeng Xia

{"title":"Protecting Sensitive Location Visits Against Inference Attacks in Trajectory Publishing","authors":"Xiangyu Liu, Manish Singh, Xiufeng Xia","doi":"10.1109/PDCAT46702.2019.00051","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00051","url":null,"abstract":"With an increasing popularity of Location-Based Services (LBSs), people's trajectories are continuously recorded and collected. The trajectory data are often shared or published for improving user experience, such as personalized recommendations and activity mining. However, releasing the trajectory data makes users' sensitive location visits vulnerable to inference attacks. In this paper, we study the problem of protecting sensitive location visits in the publication of trajectory data, assuming an adversary can do inference attacks using association rules derived from the data. We propose a methodology of anonymizing trajectories employing both generalizations and suppressions, to sanitize the trajectory data and protect sensitive location visits against inference attacks. We design a number of techniques to make our trajectory anonymizing algorithm efficient meanwhile maintaining the utility. We have conducted an empirical study to show that our algorithms can efficiently prevent inference attacks for real datasets while preserving the accuracy of aggregate querying on the published data.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126500337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

New Parallel Algorithms for All Pairwise Computation on Large HPC Clusters 大型HPC集群全对并行计算新算法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00045

T. Tang, Hao Wu, Wei Bao, Pengyi Yang, Dong Yuan, B. Zhou

{"title":"New Parallel Algorithms for All Pairwise Computation on Large HPC Clusters","authors":"T. Tang, Hao Wu, Wei Bao, Pengyi Yang, Dong Yuan, B. Zhou","doi":"10.1109/PDCAT46702.2019.00045","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00045","url":null,"abstract":"All pairwise computation is defined as performing computation between every pair of the elements in a given dataset. It is often a necessary first step in a number of bioinformatics applications. Many of such applications require multiple terabytes of main memory and take multiple peta floating point operations to complete the computation. Therefore, large HPC clusters are needed to tackle these large-scale computational problems. Conventionally designed parallel algorithms using data partitioning may have a scalability issue, i.e., for a given problem of fixed size the efficiency may decrease if the number of compute nodes is increased (Amdahl's law). In this paper we introduce a new method for parallel algorithm design. Using this method we first design an efficient one-dimensional (1D) ring algorithm and then a two-dimensional (2D) algorithm based on the 1D ring for all pairwise computation. When increasing the compute nodes, instead of reducing the block size, we make multiple copies of the original data blocks in the 1D ring and distribute them across the added compute nodes in the other dimension. By properly organizing the compute nodes the communication overhead can be reduced to a minimum in this two-dimensional setting. Experiments on a Cray XC40 HPC supercomputer show that our new algorithms are very efficient and scalable for large-scale all pairwise computation on large HPC clusters.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123825029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite Approximate Consensus for Privacy in Distributed Sensor Networks 分布式传感器网络中隐私的有限近似一致性

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00025

Matthew O'Connor, W. Kleijn

引用次数: 2

Base Station Positioning in Single-Tiered Wireless Sensor Networks 单层无线传感器网络中的基站定位

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00013

Xinchen Li, Huan Cai, Gang Liu, Kezhong Lu

{"title":"Base Station Positioning in Single-Tiered Wireless Sensor Networks","authors":"Xinchen Li, Huan Cai, Gang Liu, Kezhong Lu","doi":"10.1109/PDCAT46702.2019.00013","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00013","url":null,"abstract":"In wireless sensor networks, sensor nodes collect data from the surrounding environment and transfer it to the base station. If a sensor node cannot communicate with the base station directly, it needs to select one of the senor nodes that it can communicate with and transfer the data to it. The above process will go on until the data arrive at the base station. It may cause too many relays during the process in which the data are transferred to the base station. Some nodes may quickly run out of energy and cause the entire wireless network to fail. We build a breadth-first search spanning tree which is rooted in base station according to the connected relation between the sensor nodes and the base station. When sensor nodes transfer the data to the base station, the data follow the path between the node and root in the spanning tree towards the base station. This algorithm guarantees the number of relays in the process and the total energy consumption of the wireless sensor network are the least. Thus the choice of the base station position is vital to the energy consumption of the whole wireless sensor network. This paper proposes an algorithm for the base station placement that finds the optimal base station position by using computation geometry according to the relative relation between the senor nodes. In contrast to the grid computing we are familiar, this algorithm greatly reduces the computing time required by finding the optimal position of base station.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133275413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disk Throughput Controller for Cloud Data-Centers 云数据中心的磁盘吞吐量控制器

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00079

M. HoseinyFarahabady, Z. Tari, Albert Y. Zomaya

{"title":"Disk Throughput Controller for Cloud Data-Centers","authors":"M. HoseinyFarahabady, Z. Tari, Albert Y. Zomaya","doi":"10.1109/PDCAT46702.2019.00079","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00079","url":null,"abstract":"With the increasing popularity of virtual machine monitoring (VMM) technologies, performance variability among collocated virtual machines (VMs) can easily become a severe scalability issue. Particularly, it becomes a necessary for administrative team to control the performance degradation level in a shared environment when multiple I/O-intensive applications simultaneously request their I/O operations [1]. Nevertheless, adding several logical layers between the running applications and the physical storage system, as seen in contemporary virtualized storage devices, makes it considerably difficult to build a low overhead controlling mechanism for such systems (while each VM may running a separate operating system instance) [2]. In this paper, we propose a strategy based on control theory for managing the performance of several I/O requests, such as mean response times and read/write throughput in a consolidated environment where multiple virtual services can share access to a storage system. This scheme uses an approach for measuring the characterization of read/write performance attributes of each virtual services and also takes into account the run-time quality of service enforcement levels requested by them. This is formulated as an optimization problem where a reward function is defined to reduce the overall QoS violation incidents among all consolidated virtual services. Performance evaluation is carried out by comparing the proposed solution with the default embedded Linux controller across a range of emulated application workloads in scenarios with multiple consolidated virtual containers. The results confirm that the proposed solution can reduce the overall QoS violation incident rates in scenarios in which the platform operates at a significant traffic load comparing to the default policy in LXC engine.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Scalable Anomaly Detection Method for Blockchain Transactions using GPU 基于GPU的区块链事务可扩展异常检测方法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00039

Shin Morishima

{"title":"Scalable Anomaly Detection Method for Blockchain Transactions using GPU","authors":"Shin Morishima","doi":"10.1109/PDCAT46702.2019.00039","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00039","url":null,"abstract":"Blockchain is a distributed ledger system composed of P2P network proposed as an electronic cash system which can transfer money without a trusted third party. Blockchain has high tamper resistance by the structure which cannot modify a transaction by everyone including the creator of it. However, it also becomes a problem that Blockchain system cannot modify fraudulent transaction already approved. This problem means once an illegal transaction occurs, the damage expands. It is necessary to detect the transaction by the anomaly detection and modify it before approval in order to suppress the damage. However, existing anomaly detection methods of Blockchain need the processing for all the past transactions in Blockchain. The execution time exceeds the approval interval of the major Blockchain system (Ethereum). In this paper, we propose an anomaly detection method using a fixed size user-centric subgraph which is extracted from whole graph made from all the transactions, to prevent the increase of the execution time. Furthermore, to accelerate the anomaly detections, we propose the subgraph structure which is suitable for GPU processing so that all of the subgraph making, the feature extraction, and the anomaly detection are performed in GPU. When the number of transactions is 300 million, our proposed method archives 195 times faster than the existing GPU-based method and the execution time is shorter than the approval interval of the Ethereum. In terms of accuracy, the true positive rate is significantly higher than the existing method in the case of small scale transactions because the local anomaly can be detected by the subgraph with locality. And the rate in the case of large scale and the false positive rate are close to the existing method.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124401185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4