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vPlacer: A Co-scheduler for Optimizing the Performance of parallel Jobs in Xen 18th

vPlacer: A Co-scheduler for Optimizing the Performance of Pa...

引用

18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Jiang, Peng He, Ligang Ren, Shenyuan Chen, Zhiyan Mao, Rui Univ Warwick Dept Comp Sci Coventry CV4 7ES W Midlands England Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Xen, a popular virtualization platform which enables multiple operating systems sharing one physical host, has been widely used in various fields nowadays. Currently, the existing schedulers of Xen are initially targeting at serial jobs, which achieves a remarkable utilization of computer hardware and impressive overall performance. However, the virtualized systems are expected to accommodate both parallel jobs and serial jobs in practice, and resource contention between virtual machines results in severe performance degradation of the parallel jobs. Moreover, the physical resource is vastly wasted during the communication process due to the ineffective scheduling of parallel jobs. this paper aims to optimize the performance of the parallel jobs in Xen using the co-scheduling mechanism. In this paper, we statistically analyze the process of scheduling parallel jobs in Xen, which points out that the credit scheduler is not capable of properly scheduling a parallel job. Moreover, we propose vPlacer, a conservative co-scheduler to improve the performance of the parallel job in Xen. Our co-scheduler is able to identify the parallel jobs and optimize the scheduling process to satisfy the particularity of the parallel job. the prototype of our vPlacer is implemented, and the experimental results show that the performance of the parallel job is significantly improved and the utilization of the hardware resource is optimized.

关键词： Xen Virtualization parallel job Scheduler

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Removing Key Escrow from the LW-HIBE Scheme 15th

Removing Key Escrow from the LW-HIBE Scheme

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15th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Chen, Peixin Wang, Xiaofeng Zhao, Baokang Su, Jinshu You, Ilsun Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Korean Bible Univ Sch Informat Sci Nowon Dist South Korea

ISBN: (纸本)9783319271613;9783319271606

Hierarchical Identity-Based Encryption (HIBE) provides an efficient solution to the security problems existed in cloud storage. However, the key escrow problem, which is an inherent problem in HIBE, primarily hinders the widespread adoption of the cryptographic scheme in practice. To address the key escrow problem, this paper introduces a provably-secure escrow-free model, which employs multiple Key Privacy Authorities (KPAs) to restrict the power of Public Key Generators (PKGs) in HIBE scheme. We instantiate the model into an escrow-free HIBE scheme that is referred to as the EF-LW-HIBE scheme, based on the HIBE scheme introduced by Lewko and Waters. Utilizing the Dual System Encryption methodology, we prove that our EF-LW-HIBE scheme is IND-ID-CCA secure.

关键词： Hierarchical identity-based encryption Key escrow IND-ID-CCA Security Dual system encryption Cloud storage

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Scheduling Stochastic Tasks with Precedence Constrain on Cluster Systems with Heterogenous Communication Architecture 15th

Scheduling Stochastic Tasks with Precedence Constrain on Clu...

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15th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Liao, Qun Jiang, Shuangshuang Hei, Qiaoxiang Li, Tao Yang, Yulu Nankai Univ Coll Comp & Control Engn Tianjin 300353 Peoples R China

ISBN: (纸本)9783319271613;9783319271606

Scheduling precedence constrained stochastic tasks on heterogenous cluster systems is an important issue which impact the performance of clusters significantly. Different with deterministic tasks, stochastic task model assumes that the workload of task and quantity of data transmission between tasks are stochastic variables, which is more realistic than other task models. Scheduling model and algorithms of precedence constrained stochastic tasks attract a large number of researchers' attention recently. An algorithm SDLS (Stochastic Dynamic Level Scheduling) has been proved performing well in scheduling stochastic tasks on heterogenous clusters. However, the assumption about communication time between tasks in SDLS is much simpler than its assumptions about task computing time, which makes it cannot depict the communication cost among heterogenous links well. In this paper, it is assumed that the quantity of data communication between tasks is a stochastic variable of normal distribution, instead of assuming communication time among heterogenous links a same stochastic variable immediately. Moreover, a modified scheduling model and algorithm SDLS-HC (Stochastic Dynamic Level Scheduling on Heterogenous Communication links) are proposed. Work in this paper focus on considering much more detailed communication cost in task scheduling based on SDLS. Evaluation on many random generated tasks experiments demonstrates that SDLS-HC achieves better performance than SDLS on cluster systems with heterogenous links.

关键词： Stochastic tasks scheduling Directed acyclic graph Heterogenous clusters parallel and distributed processing Distributed system

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algorithms and architectures for parallel processing - 8th international conference, 2008, Proceedings

Algorithms and Architectures for Parallel Processing - 8th I...

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8th international conference on algorithms and architectures for parallel processing, ica3pp 2008

ISBN: (纸本)9783540695004

the proceedings contain 33 papers. the topics discussed include: smart content delivery on the Internet;parallel query processing in databases on multicore architectures;evaluation of a novel load-balancing algorithm with variable granularity;a static multiprocessor scheduling algorithm for arbitrary directed task graphs in uncertain environments;architecture aware partitioning algorithms;a simple and efficient fault-tolerant adaptive routing algorithm for meshes;fault tolerance in the biswapped network;a general approach to predict the performance order of TSP family problems;examining the feasibility of reconfigurable models for molecular dynamics simulation;parallel simulated annealing for materialized view selection in data warehousing environments;an optimization service for DSP multicomputers;and a non-blocking multithreaded architecture with support for speculative threads.

关键词： Simulated annealing

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algorithms and architectures for parallel processing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Rocco Aversa Joanna Kołodziej Jun Zhang Flora Amato Giancarlo Fortino

ISBN: (数字)9783319038896

ISBN: (纸本)9783319038889

this two volume set LNCS 8285 and 8286 constitutes the proceedings of the 13th international conference on algorithms and architectures for parallel processing , ica3pp 2013, held in Vietri sul Mare, Italy in December 2013. the first volume contains 10 distinguished and 31 regular papers selected from 90 submissions and covering topics such as big data, multi-core programming and software tools, distributed scheduling and load balancing, high-performance scientific computing, parallel algorithms, parallel architectures, scalable and distributed databases, dependability in distributed and parallel systems, wireless and mobile computing. the second volume consists of four sections including 35 papers from one symposium and three workshops held in conjunction with ica3pp 2013 main conference. these are 13 papers from the 2013 international Symposium on Advances of Distributed and parallel Computing (ADPC 2013), 5 papers of the international Workshop on Big Data Computing (BDC 2013), 10 papers of the international Workshop on Trusted Information in Big Data (TIBiDa 2013) as well as 7 papers belonging to Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-Smart CPS 2013).

关键词： Algorithm Analysis and Problem Complexity Software Engineering Computer Communication Networks Database Management

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parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs 20th

Parallel SCC Detection Based on Reusing Warps and Coloring P...

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

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

ISBN: (纸本)9783030602451;9783030602444

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. the scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. the experimental results expose that our approach can achieve 7.23x, 30.55x, 1.75x and 1.26x speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.

关键词： Strongly connected components detection GPUs Reusing warps Coloring partitions Partitions combined with traversal

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Secure and Energy-Efficient Data Aggregation with Malicious Aggregator Identification in Wireless Sensor Networks

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11th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Li, Hongjuan Li, Keqiu Qu, Wenyu Stojmenovic, Ivan Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Univ Ottawa SITE Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9783642246494

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. the goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.

关键词： Energy utilization

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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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SPMP: A JavaScript Support for Shared Persistent Memory on *** 18th

SPMP: A JavaScript Support for Shared Persistent Memory on *...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Zhang, Qipeng Li, Tianyou Deng, Pan Chen, Yuting Huang, Linpeng Rudoff, Andy Shanghai Jiao Tong Univ Shanghai Peoples R China Intel Asia Pacific R&D Co LTD Shanghai Peoples R China Intel Corp Santa Clara CA USA

ISBN: (纸本)9783030050542;9783030050535

JavaScript is widely used for scripting on client side. *** is a JavaScript runtime environment, allowing Javascript to be used for building scalable network applications on server side. However, *** does not support parallel programming, making it difficult to enhance applications' performance. Meanwhile, persistent memory (PM) shows optimistic prospects of being used in server-side applications, while few researches do exist in allowing script languages to support PM-based parallel programming. In this paper, we introduce SPMP, a JavaScript support for shared persistent memory on ***. With SPMP, each process needs to hold PersistentArrayBuffer, an object that is responsible for allocating, managing, and accessing persistent memory. Multiple processes can then share persistent memory and communicate each other by their PersistentArrayBuffer objects. Furthermore, SPMP supports dynamic load-balancing strategies and ensures data coherency, and also supports data persistence in a secondary storage. We have evaluated SPMP against Extended Memory Semantics (EMS, a state-of-the-art model for parallel programming on ***) on two data-intensive tasks. the results show that SPMP is 100 similar to 300x faster than EMS on five basic operations, and 2x faster on complicated parallel computing tasks such as counting words, due to its particular way on memory allocation and mapping.

关键词： Node.js parallel programming Shared persistent memory

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A parallel Fast Fourier Transform Algorithm for Large-Scale Signal Data Using Apache Spark in Cloud 18th

A Parallel Fast Fourier Transform Algorithm for Large-Scale ...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Yang, Cheng Bao, Weidong Zhu, Xiaomin Wang, Ji Xiao, Wenhua Natl Univ Def Technol Changsha Peoples R China State Key Lab High Performance Comp Changsha Peoples R China Acad Mil Med Sci Beijing Peoples R China

ISBN: (纸本)9783030050573;9783030050566

In the field of signal process, Fast Fourier Transform (FFT) is a widely used algorithm to transform signal data from time to frequency. Unfortunately, with the exponential growth of data, traditional methods cannot meet the demand of large-scale computation on these big data because of three main challenges of large-scale FFT, i.e., big data size, real-time data processing and high utilization of compute resources. To satisfy these requirements, an optimized FFT algorithm in Cloud is deadly needed. In this paper, we introduce a new method to conduct FFT in Cloud with the following contributions: first, we design a parallel FFT algorithm for large-scaled signal data in Cloud;second, we propose a MapReduce-based mechanism to distribute data to compute nodes using big data processing framework;third, an optimal method of distributing compute resources is implemented to accelerate the algorithm by avoiding redundant data exchange between compute nodes. the algorithm is designed in MapReduce computation framework which contains three steps: data preprocessing, local data transform and parallel data transform to integrate processing results. the parallel FFT is implemented in a 16-node Cloud to process real signal data the experimental results reveal an obvious improvement in the algorithm speed. Our parallel FFT is approximately five times faster than FFT in Matlab in when the data size reaches 10 GB.

关键词： Fast fourier transform Cloud computing Apache spark parallel algorithm

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