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Deadline-oriented task scheduling for mapreduce environments 15th

Deadline-oriented task scheduling for mapreduce environments

引用

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

Parallel training GBRT based on Kmeans histogram approximati...

引用

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

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

引用

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

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

引用

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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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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Enhancing parallel data loading for large scale scientific database 15th

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

引用

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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Character of graph analysis workloads and recommended solutions on future parallel systems

Character of graph analysis workloads and recommended soluti...

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13th international conference on algorithms and architectures for parallel processing, ica3pp 2013

作者： Tanabe, Noboru Tomimori, Sonoko Takata, Masami Joe, Kazuki Toshiba Research and Development Center Kawasaki Kanagawa 212-8582 Japan Nara Women's University Nara Nara 630-8506 Japan

ISBN: (纸本)9783319038582

Graph500 is a benchmark suite for big data analysis. Matrices used for Graph500 inherit the properties of graph analysis such as breadth first search for SNS and PageRank for web searching engine. Especially power saving is very important for its execution on future massively parallel processors and clouds. the spatial locality of sparse matrices used for Graph500 and its behaviors on cache memory are investigated. the experimental results show the spatial locality of sparse matrices used for Graph500 is very low. It is very difficult to solve the problem by just software approach because of the huge size and the randomness of their accesses. therefore, we recommend hardwired scatter/gather functions at memory side. they improve the processing speed in an order of magnitude. For achieving both of low power and high throughput of random access, we recommend implementing hardwired scatter/gather functions on logic-base in Hybrid Memory Cube (HMC). We also describe brief considerations of the power saving in the case of low cache hit rate application such as graph500. For example, when the hit rate is 15%, the power saving ratio of memory access is about 30-fold. © Springer international Publishing Switzerland 2013.

关键词： Cache memory

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parallel aware hybrid Solid-State storage 15th

Parallel aware hybrid Solid-State storage

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

作者： He, Dan Wang, Fang Feng, Dan Liu, Jingning Wu, Yunxiang He, Ying Hu, Yang Wuhan National Laboratory for Optoelectronics School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Nanchang Hangkong University Nanchang330063 China China Ship Development and Design Center Wuhan430064 China

ISBN: (纸本)9783319271392

Compared with tradition disk, NAND Flash has advantages of higher performance and shock resistance. But before write, NAND Flash must erase the old messages. that why NAND Flash based Solid State Disks (SSDs) always use the log-based schemes to improve the performance. Compared with NAND Flash, Phase Change Memory (PCM) has higher write performance, longer lifetime, and can update in-place, but its cost is high and capacity is low. So, in PCM and NAND Flash hybrid SSD, PCM is always used as log region, such as In-Page Logging-based (hybrid-IPL) SSD. the log-based SSD incurs a large number of merge operations. the cost of merge operation is very high because it involves many read, write operations, as well as an erase operation. So, how to reduce the cost of merge operations is the critical challenge to log-based hybrid (PCM and flash) SSD. In this paper, we propose a new merge scheme in PCM and NAND Flash hybrid SSD, called parallel aware hybrid In-Page Logging-based (P-aware-IPL) SSD. this scheme can exploit the die-level and plane-level parallelism of flash. Leveraging these two levels of parallelism, the cost of full merge is significantly reduced compared with that of hybrid-IPL SSD scheme and there is no other additional overhead in our algorithm. Experiment results have shown that the proposed P-aware-IPL reduces the flash write and erase operations by up to 10% and average response time by up to 22% against the hybrid-IPL scheme. © Springer international Publishing Switzerland 2015.

关键词： Cost reduction

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