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International Conference on Parallel Processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity Cloud computing Algorithm design and analysis Load modeling Resource management

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R-iCDN: An approach supporting flexible content routing for ISP-operated CDN 9

R-iCDN: An approach supporting flexible content routing for ...

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9th ACM MobiCom Workshop on Mobility in the Evolving Internet Architecture, MobiArch 2014

作者： Lin, Tao Xu, Yuemei Zhang, Guoqiang Xin, Yonghui Li, Yang Ci, Song High Performance Network Lab. Institute of Acoustics Chinese Academy of Sciences China School of Computer Science and Technology Nanjing Normal University China Department of Computer and Electronics Engineering University of Nebraska-Lincoln United States

ISBN: (纸本)9781450330749

Driven by the requirements of both accommodating the increasing traffic volume and monetizing online contents, more and more ISPs are embracing and building their own content delivery networks. Traditionally, there exist two kinds of ISP-operated CDNs from the perspective of overlay network topology, namely flat CDN and hierarchical CDN. However, we argue that both flat and hierarchical CDNs are suboptimal since they can not fully exploit the ISP network infrastructure such as rich multi-homing links. In this paper, we borrow the idea of name-based routing from Information-centric Networking and propose a novel ISP-operated CDN approach which supports flexible content routing to optimize the efficiency of content distribution. Extensive experiments carried out on a real nation-scale network topology have been performed to evaluate the proposed approach, and results demonstrate that our proposed approach can significantly improve the performance.

关键词： Network topology

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Characterizing the performance-energy tradeoff of small ARM Cores in HPC computation 1

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20th International Conference on Parallel Processing, Euro-Par 2014

作者： Laurenzano, Michael A. Tiwari, Ananta Jundt, Adam Peraza, Joshua Ward Jr., William A. Campbell, Roy Carrington, Laura EP Analytics United States Dept. of Computer Science and Engineering University of Michigan United States Performance Modeling and Characterization Lab San Diego Supercomputer Center United States High Performance Computing Modernization Program U.S. Dept. of Defense United States

ISBN: (数字)9783319098739

ISBN: (纸本)9783319098722

Deploying large numbers of small, low-power cores has been gaining traction recently as a system design strategy in high performance computing (HPC). The ARM platform that dominates the embedded and mobile computing segments is now being considered as an alternative to high-end x86 processors that largely dominate HPC because peak performance per watt may be substantially improved using off-the-shelf commodity processors. In this work we methodically characterize the performance and energy of HPC computations drawn from a number of problem domains on current ARM and x86 processors. Unsurprisingly, we find that the performance, energy and energy-delay product of applications running on these platforms varies significantly across problem types and inputs. Using static program analysis we further show that this variation can be explained largely in terms of the capabilities of two processor subsystems: single instruction multiple data (SIMD)/floating point and the cache/memory hierarchy;and that static analysis of this kind is sufficient to predict which platform is best for a particular application/input pair. In the context of these findings, we evaluate how some of the key architectural changes being made for upcoming 64-bit ARM platforms may impact HPC application performance. © 2014 Springer International Publishing Switzerland.

关键词： Static analysis

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Comparison of high Level FPGA Hardware Design for Solving Tri-diagonal Linear Systems

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Procedia computer science 2014年 29卷 95-101页

作者： David J. Warne Neil A. Kelson Ross F. Hayward High Performance Computing and Research Support Queensland University of Technology Brisbane Queensland Australia School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Queensland Australia

Reconfigurable computing devices can increase the performance of compute intensive algorithms by implementing application specific co-processor architectures. The power cost for this performance gain is often an order of magnitude less than that of modern CPUs and GPUs. Exploiting the potential of reconfigurable devices such as Field-Programmable Gate Arrays (FPGAs) is typically a complex and tedious hardware engineering task. Recently the major FPGA vendors (Altera, and Xilinx) have released their own high-level design tools, which have great potential for rapid development of FPGA based custom accelerators. In this paper, we will evaluate Altera's OpenCL Software Development Kit, and Xilinx's Vivado high Level Sythesis tool. These tools will be compared for their performance, logic utilisation, and ease of development for the test case of a tri-diagonal linear system solver.

关键词： Reconfigurable computing Field Programmable Gate Arrays high Level Synthesis Open computing Language Numerical Linear Algebra Tri-diagonal Linear Systems

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Incorporating language patterns and domain knowledge into feature-opinion extraction

Incorporating language patterns and domain knowledge into fe...

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17th International Conference on Text, Speech, and Dialogue, TSD 2014

作者： Zhou, Erqiang Luo, Xi Qin, Zhiguang School of Computer Science and Engineering University of Electronic Science and Technology of China No.2006 Xiyuan Ave Chengdu 611731 Sichuan China Guangdong Key Laboratory of Popular High Performance Computers Shenzhen Key Laboratory of Service Computing and Applications No. 3688 Nanhai Ave Shenzhen 518060 Guangdong China

ISBN: (纸本)9783319108155

We present a hybrid method for aspect-based sentiment analysis of Chinese restaurant reviews. Two main components are employed so as to extract feature-opinion pairs in the proposed method: domain independent language patterns found in Chinese and a lexical base built for restaurant reviews. The language patterns focus on the general knowledge which is implicit contained in Chinese, thus can be used directly by other domains without any modification. The lexical base, on the other hand, targets for particular characteristics of a given domain and acts as a plug-in part in our prototype system, thus does not affect the portability when applying the proposed approach in practice. Empirical evaluation shows that our method performs well and it can gain a progressive result when each component takes into effective. © 2014 Springer International Publishing.

关键词： Sentiment analysis

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Scaling graph community detection on the Tilera many-core architecture

Scaling graph community detection on the Tilera many-core ar...

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International Conference on high performance computing

作者： Daniel Chavarria-Miranda Mahantesh Halappanavar Ananth Kalyanaraman High Performance Computing Pacific Northwest National Laboratory Richland WA School of Electrical Engineering and Computer Science Washington State University Pullman WA

In an era when power constraints and data movement are proving to be significant barriers for the application of high-end computing, the Tilera many-core architecture offers a low-power platform exhibiting many important characteristics of future systems, including a large number of simple cores, a sophisticated network-on-chip, and fine-grained control over memory and caching policies. While this emerging architecture has been previously studied for structured compute-intensive kernels, benchmarking the platform for data-bound, irregular applications present significant challenges that have remained unexplored. Community detection is an advanced prototypical graph-theoretic operation with applications in numerous scientific domains including life sciences, cyber security, and power systems. In this work, we explore multiple design strategies toward developing a scalable tool for community detection on the Tilera platform. Using several memory layout and work scheduling techniques we demonstrate speedups of up to 47× on 36 cores of the Tilera TileGX36 platform over the best serial implementation, and also show results that have comparable quality and performance to mainstream x86 platforms. To the best of our knowledge this is the first work addressing graph algorithms on the Tilera platform. This study demonstrates that through careful design space exploration, low-power many-core platforms like Tilera can be effectively exploited for graph algorithms that embody all the essential characteristics of an irregular application.

关键词： Communities Image color analysis Layout Convergence Multicore processing Resource management

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GIRAFFE: A scalable distributed coordination service for large-scale systems

GIRAFFE: A scalable distributed coordination service for lar...

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IEEE International Conference on Cluster computing

作者： Xuanhua Shi Haohong Lin Hai Jin Bing Bing Zhou Zuoning Yin Sheng Di Song Wu Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Centre for Distributed and High Performance Computing School of Information Technologies University of Sydney GraphSQL Inc. INRIA and Argonne National Laboratory

ISBN: (纸本)9781479955497

The scale of cloud services keeps increasing over time, significantly introducing huge challenges in system manageability and reliability. Designing coordination services in cloud is the right track to solve the above problems. However, existing coordination services (e.g., Chubby and ZooKeeper) only perform well in read-intensive scenario and small ensemble scales. To this end, we propose Giraffe, a scalable distributed coordination service. There are three important contributions in our design. (1) Giraffe organizes coordination servers using interior-node-disjoint trees for better scalability. (2) Giraffe employs a novel Paxos protocol for strong consistency and fault-tolerance. (3) Giraffe supports hierarchical data organization and in-memory storage for high throughput and low latency. We evaluate Giraffe on a high performance computing test-bed. The experimental results show that Giraffe gains much better write performance than ZooKeeper when server ensemble is large. Giraffe is nearly 300% faster than ZooKeeper on update operations when ensemble size is 50 servers. Experiments also show that Giraffe reacts and recovers more quickly than ZooKeeper against node failures.

关键词： cloud computing fault tolerant computing large-scale systems protocols public domain software reliability Giraffe Paxos protocol ZooKeeper cloud services fault-tolerance hierarchical data organization memory storage scalable distributed coordination service Color Proposals Protocols Scalability Servers Topology Vegetation Consistency Coordination Service Distributed Cloud System Fault Tolerance

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Programming for scientific computing on peta-scale heterogeneous parallel systems

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Journal of Central South University 2013年第5期20卷 1189-1203页

作者：杨灿群吴强唐滔王锋薛京灵 State Key Laboratory of High Performance Computing(National University of Defense Technology) School of Computer Science and Engineering University of New South Wales

Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.

关键词： heterogeneous parallel system programming framework scientific computing GPU computing molecular dynamic

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Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks

Parallel Bayesian Network Structure Learning for Genome-Scal...

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Supercomputing Conference

作者： Sanchit Misra Md. Vasimuddin Kiran Pamnany Sriram P. Chockalingam Yong Dong Min Xie Maneesha R. Aluru Srinivas Aluru Parallel Computing Lab Intel Corporation Bangalore India Dept. of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China School of Biology Georgia Institute of Technology Atlanta USA School of Computational Science and Engineering Georgia Institute of Technology Atlanta USA

Learning Bayesian networks is NP-hard. Even with recent progress in heuristic and parallel algorithms, modeling capabilities still fall short of the scale of the problems encountered. In this paper, we present a massively parallel method for Bayesian network structure learning, and demonstrate its capability by constructing genome-scale gene networks of the model plant Arabidopsis thaliana from over 168.5 million gene expression values. We report strong scaling efficiency of 75% and demonstrate scaling to 1.57 million cores of the Tianhe-2 supercomputer. Our results constitute three and five orders of magnitude increase over previously published results in the scale of data analyzed and computations performed, respectively. We achieve this through algorithmic innovations, using efficient techniques to distribute work across all compute nodes, all available processors and coprocessors on each node, all available threads on each processor and coprocessor, and vectorization techniques to maximize single thread performance.

关键词： Bayes methods Hypercubes Instruction sets Genomics Bioinformatics Coprocessors Vectors

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Parallel architecture and optimization for discrete-event simulation of spike neural networks

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science China(Technological sciences) 2013年第2期56卷 509-517页

作者： TANG YuHua ZHANG BaiDa WU JunJie HU TianJiang ZHOU Jing LIU FuDong Department of Computer Science and Technology School of ComputerNational University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology College of Mechatronic Engineering and Automation National University of Defense Technology

Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated ***,spike neural networks are usually used on a large scale,and they cannot be computed on commercial,off-the-shelf computers.A parallel architecture is proposed and developed for discrete-event simulations of spike neural ***,mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational *** results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.

关键词： spike neural network discrete event simulation intelligent parallelization framework

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