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Addressing NoC reliability through an efficient fibonacci-based crosstalk avoidance codec design 15th

Addressing NoC reliability through an efficient fibonacci-ba...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Shirmohammadi, Zahra Miremadi, Seyed Ghassem International Campus Sharif University of Technology Kish Island Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9783319271361

the reliable transfer in Network on Chips (NoCs) can be threatened by crosstalk fault occurring in wires. Crossstalk fault is due to inter-wire coupling capacitance that based on the patterns of transitions appearing on the wires, significantly limits the reliability of NoCs. Among these transitions, 101 and 010 bit patterns impose the worst crosstalk effects to wires. this work intends to increase the reliability of NoCs against crosstalk faults by applying an improved Fibonacci-based numeral system, called Doubled-Penultimate Fibonacci (DP-Fibo). In the DP-Fibo coding algorithm, code words without ‘101’ and ‘010’ bit patterns are produced to reduce crosstalk faults. Experimental results show that the proposed numerical system: (1) can be utilized in NoC channels with any arbitrary wire width and, (2) can outperform other existing coding mechanism in providing significant reliability improvement and reduction over the area occupation and power consumption of NoCs. Experiments indicated that DP-Fibo provides improvement in area occupation, power-delay product, critical path and power consumptions of codec with respect to the state-of-the-art Fibonacci coding mechanism by 13%, 22.7%, 5% and 25% respectively. © Springer international Publishing Switzerland 2015.

关键词： Network-on-chip

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Enhancing parallel data loading for large scale scientific database 15th

Enhancing parallel data loading for large scale scientific d...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Li, Hui Li, Hongyuan Chen, Mei Dai, Zhenyu Zhu, Ming Huang, Menglin Department of Computer Science Guizhou University Guiyang550025 China Guizhou Engineering Laboratory of ACMIS Guiyang550025 China National Astronomical Observatories Chinese Academy of Sciences Beijing100016 China

ISBN: (纸本)9783319271217

the rapidly increased data size make large scale scientific database often have a huge time delay between loading data into the system and ready for receiving query request. To solve this problem, we proposed an efficient parallel data loading approach named FASTLoad. It is designed to maximize the given resource (e.g., network bandwidth, main memory) utilization for optimizing the data loading in large scale array model based scientific database system. To verify the efficiency of FASTLoad, we implemented it in our Adaptable Data Loading System and evaluate its performance over various sizes of large scientific data sets. Our experimental results show that the performance of FASTLoad can be 4 to 6 times fast than the built-in loading techniques of states-of-the-arts array model based scientific database system. © Springer international Publishing Switzerland 2015.

关键词： Database systems

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parallel training GBRT based on Kmeans histogram approximation for big data 15th

Parallel training GBRT based on Kmeans histogram approximati...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Gu, Rong Jin, Lei Wu, Yongwei Qu, Jingying Wang, Tao Wang, Xiaojun Yuan, Chunfeng Huang, Yihua National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University Nanjing210023 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9783319271217

Gradient Boosting Regression Tree (GBRT), one of the stateof- the-art ranking algorithms widely used in industry, faces challenges in the big data era. With the rapid increase in the sizes of datasets, the iterative training process of GBRT becomes very time-consuming over large scale data. In this paper, we aim to speed up the training process of each tree in the GBRT framework. First, we propose a novel KMeans histogram building algorithm which has lower time complexity and is more efficient than the cutting-edge histogram building method. Further, we put forward an approximation algorithm by combining the kernel density estimation with the histogram technique to improve the accuracy. We conduct a variety of experiments on both the public Learning To Rank(LTR) benchmark datasets and the large-scale real-world datasets from Baidu search engine. the experimental results show that our proposed parallel training algorithm outperforms the state-of-the-art parallel GBRT algorithm with near 2 times speedup and better accuracy. Also, our algorithm achieves the near-linear scalability. © Springer international Publishing Switzerland 2015.

关键词： Search engines

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Identifying repeated interleavings to improve the efficiency of concurrency bug detection 15th

Identifying repeated interleavings to improve the efficiency...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Wu, Zhendong Lu, Kai Wang, Xiaoping Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783319271392

Detecting concurrency bugs is becoming increasingly important. Many pattern-based concurrency bug detectors focus on the specific types of interleavings that are correlated to concurrency bugs. To detect multiple types of concurrency bugs, general detectors focus on multiple types of interleaving patterns, including data race pattern, atomicity violation pattern, and atomic-set violation pattern. Unfortunately, they suffer from redundant analysis due to repeated interleavings, which may affect the efficiency of concurrency bug detection. Hence, we propose an approach to identify the repeated interleavings. To the best of our knowledge, this is the first approach that can prune repeated interleavings to improve the efficiency of concurrency bug detection. We apply our approach to existing general detectors (PECAN and Maple) to avoid analyzing repeated interleavings. We evaluated the general detectors with and without our approach, respectively. the experimental results show that the bug detection results are not affected. With our approach, the bug detection time of PECAN and Maple are reduced by 40.0% and 44.4%, respectively. Additionally, our approach does not affect the overhead of bug detection, and consumes only a little memory. © Springer international Publishing Switzerland 2015.

关键词： Efficiency

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DFIS: A scalable distributed fingerprint identification system 15th

DFIS: A scalable distributed fingerprint identification syst...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Zhao, Yunxiang Zhang, Wanxin Li, Dongsheng Huang, Zhen National Laboratory for Parallel and Distributed Processing College of Computer National University of Defence Technology ChangshaHunan410073 China

ISBN: (纸本)9783319271361

Fingerprint has been widely used in a variety of biometric identification systems. However, with the rapid development of fingerprint identification systems, the amount of fingerprints information stored in systems has been rising sharply, making it challenging to process and store fingerprints efficiently and robustly with traditional standalone systems and relational databases. In this paper, we propose a scalable distributed fingerprint identification system, named DFIS. It combines the feature extraction procedure with HIPI library and optimizes the load balance strategy of MongoDB to construct a much more robust and stable system. Related experiments and simulations have been carried out and results show that DFIS can reduce the time expense by 70% during the features extraction procedural. For load balance of MongoDB, DFIS can decrease the difference of access load to less than 5% and meanwhile decrease 50% data migration to gain more reasonable distribution of operation load and data load among shards compared with the default load balance strategy in MongoDB. © Springer international Publishing Switzerland 2015.

关键词： Distributed computer systems

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Deadline-oriented task scheduling for mapreduce environments 15th

Deadline-oriented task scheduling for mapreduce environments

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Hu, Minghao Wang, Changjian You, Pengfei Huang, Zhen Peng, Yuxing National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410072 China

ISBN: (纸本)9783319271217

To provide timely results for ‘Big Data Analytics’, it is crucial to satisfy deadline requirements for MapReduce jobs in production environments. In this paper, we propose a deadline-oriented task scheduling approach, named Dart, to meet the given deadline and maximize the input size if only part of the dataset can be processed before the time limit. Dart uses an iterative estimation method which is based on both historical data and job running status to precisely estimate the realtime job completion time. By comparing the estimated time with the deadline constraint, a YARN-based task scheduler dynamically decides whether continuing or terminating the map *** have validated our approach using workloads from OpenCloud and Facebook on a cluster of 60 virtual machines. the results show that Dart can not only effectively meet the deadline but also process near-maximal data volumes even when the deadline is set to be extremely small and limited resources are allocated. © Springer international Publishing Switzerland 2015.

关键词： MapReduce

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Joint redundancy and inspection-based maintenance optimization for series-parallel system 15th

Joint redundancy and inspection-based maintenance optimizati...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Yao, Yuan He, Pan Zheng, Zhihao Tan, Chun Yuan, Yue Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

ISBN: (纸本)9783319271361

While researchers have concentrated on the optimization of joint redundancy and maintenance mechanism, maintenance in computing systems is quite different from that in traditional systems. Considering a routine monitoring and inspection mechanisms is conducted to detect component status and trigger repair process, this paper pays attention to the optimization problem of joint redundancy and inspection-based maintenance mechanism. After conducting steady state analysis on subsystems using inspection-based maintenance, shared repair facility and component redundancy, optimization model is built to search appropriate system structure and maintenance policy which maximizes system performance while meeting availability and cost constraints. Due to the complexity of uncertain optimization model, genetic algorithm is used to search optimal solution, using triple-element encoding mechanism and specifically designed operators. Illustrative examples are conducted to show that the optimization model and corresponding solution technique could be used to search optimal system configuration under given constraints and different cost constraints would lead to different optimization result while meeting availability constraints. © Springer international Publishing Switzerland 2015.

关键词： Redundancy

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parallel Kirchhoff pre-stack depth migration on large high performance clusters 15th

Parallel Kirchhoff pre-stack depth migration on large high p...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Li, Chao Wang, Yida Zhao, Changhai Yan, Haihua Zhang, Jianlei School of Computer Science and Engineering Beihang University Beijing100191 China Research and Development Center BGP Inc. CNPC ZhuozhouHebei072751 China

ISBN: (纸本)9783319271361

Kirchhoff Pre-Stack Depth Migration (KPSDM) is a widely used algorithm for seismic imaging in petroleum industry. To provide higher FLOPS, modern high performance clusters are equipped with more computing nodes and more cores for each node. the evolution style of clusters leads to two problems for upper layer applications such as KPSDM: (1) the increasing disparity of the I/O capacity and computing performance is becoming a bottleneck for higher scalability;(2) the decreasing Mean Time Between Failures (MTBF) limits the availability of the applications. In this paper, we present an optimized parallel implementation of KPSDM to adapt to modern clusters. First, we convert the KPSDM into a clear and simple task-based parallel application by decomposing the computation along two dimensions: the imaging space and seismic data. then, those tasks are mapped to computing nodes that are organized using a two-level master/worker architecture to reduce the I/O workloads. And each task is further parallelized using multi-cores to fully utilize the computing resources. Finally, fault tolerance and checkpoint are implemented to meet the availability requirement in production environments. Experimental results with practical seismic data show that our parallel implementation of KPSDM can scale smoothly from 51 nodes (816 cores) to 211 nodes (3376 cores) with low I/O workloads on the I/O sub-system and multiple process failures can be tolerated efficiently. © Springer international Publishing Switzerland 2015.

关键词： Fault tolerance

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parallel data regeneration based on multiple trees with network coding in distributed storage system 15th

Parallel data regeneration based on multiple trees with netw...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： You, Pengfei Huang, Zhen Wang, Changjian Hu, Minghao Peng, Yuxing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783319271217

Distributed storage systems can provide large-scale data storage and high data reliability by redundant schemes, such as replica and erasure codes. Redundant data may get lost due to frequent node failures in the system. the lost data is needed to be regenerated as soon as possible so as to maintain data availability and reliability. the direct way for reducing regeneration time is to reduce network traffic in regeneration. Compared with that way, tree-structured regeneration achieves shorter regeneration time by constructing better treestructured topology to increase transmission bandwidth. However, some bandwidth of many other edges beyond the tree is not utilized to speed up transmission in tree-structured regeneration. In this paper, we consider to use multiple edge-disjoint trees to parallel regenerate the lost data, and analyze the total regeneration time. We deduce the formula about optimal regeneration time, and propose an approximate construction algorithm with polynomial time complexity for the optimal multiple regeneration trees. Our experiments shows, the regeneration time reduces 62 % compared with common tree–structured scheme, and the file availability reaches almost 99 %. © Springer international Publishing Switzerland 2015.

关键词： Network coding

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Unified virtual memory support for deep CNN accelerator on soC FPGA 1

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Xiao, Tao Qiao, Yuran Shen, Junzhong Yang, Qianming Wen, Mei College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

Cooperation of CPU and hardware accelerator on SoC FPGA to accomplish computational intensive tasks, provides significant advantages in performance and energy efficiency. However, current operating systems provide little support for accelerators: the OS is unaware that a computational task can be executed either on a CPU core or an accelerator, and provides no assistance in efficient management of data sharing between CPU and accelerator on the DRAM, such as zero copy, data coherence. It’s also hard for current OS to allocate large contiguous physical memory space for accelerator. In this paper, we select the Xilinx ZYNQ as target and qualitatively analyze methods of sharing data. Besides using high-performance (HP) AXI interfaces of the ZYQN device, we develop a novel memory management system for FPGA-based accelerator. It provides a unified virtual space for CPU cores and accelerator so that they can access the same memory space in the operating systems user space. For a deep convolutional neural network task, our design gains up to speed-up of 5. 34x compared to traditional processoraccelerator cooperation. © Springer international Publishing Switzerland 2015.

关键词： Energy efficiency

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