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Implementation of global digital library grid platform using data grid technology

Implementation of global digital library grid platform using...

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作者： Yang, Chao-Tung Ho, Hsin-Chuan Yang, I.-Hsien High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Lizen High School Taichung Taiwan Knowledge and Service Information Corporation Taichung City Taiwan

Since the beginning of the 21st century, knowledge has been accumulating at a tremendous speed. The amount of global books, literature, and data available has increased in astounding quantities. The traditional library service system is no longer able to meet the requirements of the present reality. With the development of Internet and rapid advancement of information technology, a totally new knowledge transfer model has been developed: Books and other forms of data (including text, voice and images) are digitized and then provided to readers by way of a more convenient method, in order to search for and obtain the exact content they need from libraries. We propose a concept for a Global Digital Library Grid. This digital library organization would not only include new members (new libraries or new museums) but be flexible enough not to affect the systems and framework of the original Grid member. As the organization grows, it will form an enormous Virtual Grid Digital Library to provide readers with speedy Knowledge Service that meets their requirements.

关键词： Digital libraries

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Submesh allocation in heterogeneous chip multiprocessors

Submesh allocation in heterogeneous chip multiprocessors

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International Conference on Machine Learning and Cybernetics

作者： Zuo, Qi Qiu, Jing High Performance Embedded Computing Library School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Department of information science and engineering Hebei University of Science and Technology Shijiazhuang 050018 China

ISBN: (纸本)9781424465262

Lots of submesh allocation strategies are devised in mesh connected computers. In chip multiprocessors connected by network on chip, submesh allocation is also used to allocate processors. Existing research mainly focuses on fragmentation, system performance and algorithmic complexity. The processors are assumed to be homogeneous, and the representation of them only reflects the state of processors usage, free or allocated. However, heterogeneous cores have been widely adopted in modern chip multiprocessors to better support the different computation needs. Therefore, the submesh allocation should be re-designed to reflect the difference between cores in heterogeneous chip multiprocessors. In this paper, a simple submesh allocation strategy based on processor array is presented. The strategy represents the heterogeneous processors by computing power array and usage bitmap. The jobs are represented by needed computing power array. A constrained first fit algorithm is devised to map jobs to the chip multiprocessors. © 2010 IEEE.

关键词： computing power

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Cloud-aware processing of MapReduce-based OLAP applications

Cloud-aware processing of MapReduce-based OLAP applications

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11th Australasian Symposium on Parallel and Distributed computing, AusPDC 2013

作者： Han, Hyuck Lee, Young Choon Choi, Seungmi Yeom, Heon Y. Zomaya, Albert Y. School of Computer Science and Engineering Seoul National University Seoul 151-742 Korea Republic of Centre for Distributed and High Performance Computing School of Information Technologies University of Sydney NSW 2006 Australia

ISBN: (纸本)9781921770258

As the volume of data to be processed in a timely manner soars, the scale of computing and storage systems has much trouble keeping up with such a rate of explosive data growth. A hybrid cloud combining two or more clouds is emerging as an appealing alternative to expand local/private systems. However, the effective use of such an expanded cloud system is limited primarily by low network bandwidth and high latency between clouds (i.e., large intercloud data transmission overheads) when applications/services span across clouds, and they deal with large data in particular. Online analytical processing (OLAP) applications are a typical class of data-intensive application. These applications process multi-dimensional analytical queries dealing with 'big data' (or data warehouses). In this paper, we address the effective processing of MapReduce-based OLAP applications in a hybrid-cloud environment, and present a (hybrid) cloud-aware OLAP system incorporating data filtering techniques. Our system filters out unnecessary data for intercloud transmission with the ultimate goal of optimizing the performance to cost ratio, or cost efficiency. Based on experimental results obtained using two large-scale data analysis benchmarks, our system demonstrates its efficacy in improving the cost efficiency with the reduction in intercloud network traffic from 76%-99%. © 2013 Australian computer Society, Inc.

关键词： MapReduce

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Towards Harmless Rawlsian Fairness Regardless of Demographic Prior 38

Towards Harmless Rawlsian Fairness Regardless of Demographic...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024 Neural information processing systems foundation. All rights reserved.

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An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments

An effective data locality aware task scheduling method for ...

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2011 International Conference on Cloud and Service computing, CSC 2011

作者： Zhang, Xiaohong Feng, Yuhong Feng, Shengzhong Fan, Jianping Ming, Zhong School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454000 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China National High Performance Computing Center at Shenzhen Shenzhen 518055 China

ISBN: (纸本)9781457716362

Data locality has recently been extensively exploited in Cloud computing to improve system performance. However, when schedule Map tasks in Hadoop MapReduce framework working in a heterogeneous environment, existing methods either cannot reduce the occurrence of these Map tasks or injure fairness, thus degrading the system performance. In order to address this problem, this paper proposes a data locality aware scheduling method to improve the Hadoop MapReduce system performance in heterogeneous computing environments. After receiving a request from a requesting node, our method preferentially schedules the task whose input data is stored on the requesting node. If no such tasks exist, our method will select the task whose input data is nearest to the requesting node, and then make a decision on whether to reserve the task for the node storing the input data or schedule the task to the requesting node by transferring the input data to the requesting node on the fly. As a proof of concept, we implement the method in Hadoop-0.20.2. In order to evaluate the performance, we carry out an experimental comparison study on our proposed method against the default scheduling method used in Hadoop-0.20.2. The experiment results show that our proposed method improves the data locality and reduces the normalized execution time as well as the response time of jobs. © 2011 IEEE.

关键词： MapReduce

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PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning 23

PEPC: Parallel and Extensible PC Implementation for Causal S...

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7th International Conference on high performance Compilation, computing and Communications, HP3C 2023

作者： Feng, Yongquan Xu, Liyang Huang, Wanrong Yang, Wenjing Lu, Yutong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450399883

Learning the causal relationships among variables from observational data has been a significant problem in statistics and data mining, and one such algorithm is the PC algorithm. However, the existing CPU-based implementations are difficult to support Causal Structure Learning(CSL) on complex observational data due to inefficiency. In this paper, we propose an efficient CPU-based parallel implementation of the PC algorithm, called PEPC. It has an extensible framework, and some optimizations are adopted to reduce the CSL computation of the PC algorithm. Moreover, our PEPC supports efficient multi-thread parallelism. The experimental results demonstrate that PEPC outperforms existing methods in serial efficiency and multi-thread parallelism. In serial tests, our method achieves the maximum speedup ratios of 577.26, 116.74, and 134.68 over three existing approaches, Stable, ***, and Tetrad. In parallel experiments, it achieves 10.66X speedup when running the PC algorithm with 12 threads. © 2023 ACM.

关键词： Learning algorithms

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Design and implementation of an efficient multi-cluster GridRPC system

Design and implementation of an efficient multi-cluster Grid...

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2005 IEEE International Symposium on Cluster computing and the Grid, CCGrid 2005

作者： Ho, Quoc-Thuan Cai, Wentong Ong, Yew-Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore Science Park II 1 Science Park Road Singapore 117528 Singapore Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)0780390741

Multi-cluster Grids have emerged as the most popular type of Grid environments. On multi-cluster Grid, resources are distributed across different networks. Hence, scheduling and dispatching jobs are difficult and inefficient due to the restrictions of network and the communication over-head. This paper presents the design and implementation of an efficient GridRPC system for the multi-cluster Grid. Metascheduling mechanism is developed to distribute jobs onto different clusters of computers effectively. Further, a dynamic bundling mechanism is provided to reduce overhead of job dispatching. This mechanism bundles similar requests to form a composite request that is dispatched to a Grid resource. The above mechanisms have been tested with different types of services and loads, and the results show that they help to achieve more balanced workload and reduce the overhead of job dispatching. This cuts down execution time of applications. © 2005 IEEE.

关键词： Distributed computer systems

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A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： Distributed parameter control systems

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Grid-based PSE for engineering of Materials (GPEM) 07

Grid-based PSE for Engineering of Materials (GPEM)

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7th IEEE International Symposium on Cluster computing and the Grid, CCGrid 2007

作者： Salahuddin, Mohamed Hung, Terence Soh, Harold Sulaiman, Endang Soon, Ong Yew Sung, Lee Bu Yunxia, Ren Institute of High Performance Computing Singapore Science Park II 1 Science Park Rd Singapore 117528 Singapore Nanyang Technological University School of Computer Engineering Nanyang Avenue Singapore 639798 Singapore

ISBN: (纸本)0769528333

The design and engineering of complex materials and products often requires intricate interactions between domain experts in science, material and engineering as well as the utilization of diverse software systems for discovery and optimization. If left as it is, design engineers would most likely be at a loss on how to engage the entire entourage of the multi-disciplinary processes as well as the compute-intensive and data-intensive nature of the activities involved. This paper describes a possible solution through the development of a Grid-based Problem Solving Environment for engineering of Materials (GPEM). The GPEM aims to provide a one-stop platform where engineers will perform material discovery, design optimization and material characterization, with grid computing as the enabling technology. Upon describing the details of the process workflow and the adopted architecture design, the paper will present the current implementation of GPEM, in the design optimization of fractal structures. © 2007 IEEE.

关键词： Distributed computer systems

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PancakeFS: A Write Efficiently and Read Optimized Filesystem 4

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Wang, Yongkang Liu, Yang Chen, Zhiguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Institute for Quantum Information College of Computer National University of Defense Technology State Key Laboratory of High-Performance Computing Changsha China

ISBN: (纸本)9798350384062

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance. © 2024 IEEE.

关键词： Data handling

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