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A peak performance model for Matrix multiplication on general-purpose DSP

Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2013年第11 SUPPL.期40卷 148-152页

作者： Liu, Jie Chi, Li-Hua Xie, Lin-Chuan Wang, Yang Gan, Xin-Biao Feng, Hua Hu, Qing-Feng Science and Technology on Parallel and Distributed Processing Laboratory National Univ of Defense Technology Changsha Hunan 410073 China

DSP processor can be used to solve the high performance computation problems, which has the characteristics of high computing performance and low power. Matrix multiplication algorithm is the kernel of many scientific and technology computation, so it is of importance for theorem and practice. Based on general purpose DSP (GPDSP), a new parallel algorithm for matrix multiplication was proposed. And a peak performance model for matrix multiplication was built. From the peak performance model, an architecture of GPDSP was set up, and the parameter of GPDSP with Tflops was given, which includes the number of pipe-line, the number of SIMD registers, the breadth and latency for the hierarchical memories.

关键词： parallel algorithms

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A novel anycast-based integrated routing protocol for wireless sensor networks: Design and implementation

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Journal of Computational Information Systems 2013年第21期9卷 8611-8618页

作者： Yan, Guofeng Peng, Yuxing Chen, Shuhong School of Computer and Communication Hunan Institute of Engineering China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China School of Computer National University of Defense Technology Changsha 410073 China School of Information Science and Engineering Central South University Changsha 410083 China

In this paper, we consider novel anycast-based integrated routing protocol (AIRP) to reduce the cost in delay performance of communications in multihop WSNs. Without tight time synchronization or known geographic information, AIRP provides low-delay cost route. We implement a low-overhead AIRP module in TinyOS kernel by modifying BLIP protocol stack, i. e., the Berkeley Low-power IP stack;as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. We describe the format of AIRP message, the dynamic updating process of MAP table information, and anycast data flow in detail under TinyOS. And then, we present the anycast group management system. Finally, we show the performance of AIRP where AIRP communication is used to distribute load among a set of servers through a study case. © 2013 Binary Information Press.

关键词： Wireless sensor networks

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Device View Redundancy: an adaptive low-overhead fault tolerance mechanism for many-core system

Device View Redundancy: an adaptive low-overhead fault toler...

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International Workshop on Intelligent Communication and Social Networks

作者： Wentao Jia Chunyuan Zhang Jian Fu National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Institute for Informatics University of Amsterdam

ISBN: (纸本)9781479909735

Continued increasing of fault rate in integrate circuit makes processors more susceptible to errors, especially many-core processor. Meanwhile, most systems or applications do not need full fault coverage, which has excessive overhead. So on-demand fault tolerance is desired for these applications. In this paper, we propose an adaptive low-overhead fault tolerance mechanism for many-core system, called Device View Redundancy (DVR). It treats fault tolerance as a device that can be configured and used by application when high reliability is needed. Nevertheless, DVR exploits the idle resources for low-overhead fault tolerance, which is based on the observation that the utilization of many-core system is low due to lack of parallelism in application. Finally, the experiment shows that the performance overhead of DVR is reduced by 16% to 98% compared with full Dual Modular Redundancy (DMR).

关键词： On-demand redundancy Idle resource exploitation Dynamic coupling Low-overhead Many core system

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GPS: A General Framework for parallel Queries over Data Streams in Cloud

GPS: A General Framework for Parallel Queries over Data Stre...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xiaoyong Li Yijie Wang Yu Zhao Yuan Wang Xiaoling Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan P. R. China

parallel query processing over data streams in cloud computing environments has attracted considerable attention recently in various fields, due to the huge potential value of analyzing massive data or big data in a large number of streaming applications. Nevertheless, existing studies on queries primarily focus on the algorithms for the specific query types with the lack of the general framework for processing various queries. Moreover, existing parallel frameworks in cloud such as MapReduce and its variations are not suitable for many complex queries over complex data streams. In this paper, we extensively discuss the problem of designing the general framework for parallel queries over data streams in cloud. Particularly, we propose and implement a framework called GPS, which can be well adapted to various queries over complex data streams like the uncertain data streams. Furthermore, we further propose a hierarchical and general parallel model for queries over data streams based on the proposed framework, which is more flexible than the MapReduce model. The skyline queries over uncertain data streams based on our proposed framework with real deployment are conducted as an example to verify the performances of our proposals.

关键词： Peer-to-peer computing parallel processing Data models Query processing Global Positioning System Object oriented modeling distributed databases

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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Mining Software Profile across Multiple Repositories for Hierarchical Categorization

Mining Software Profile across Multiple Repositories for Hie...

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International Conference on Software Maintenance (ICSM)

作者： Tao Wang Huaimin Wang Gang Yin Charles X. Ling Xiang Li Peng Zou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Computer Science The University of Western Ontario London Ontario Canada Academy of Equipment Beijing China

ISBN: (纸本)9781467352185

The large amounts of software repositories over the Internet are fundamentally changing the traditional paradigms of software maintenance. Efficient categorization of the massive projects for retrieving the relevant software in these repositories is of vital importance for Internet-based maintenance tasks such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, which are only verified on relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software maintenance requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles across multiple repositories. We design a SVM-based categorization framework to classify the massive number of software hierarchically. To improve the categorization performance, we aggregate different types of profile attributes from multiple repositories and design a weighted combination strategy which assigns greater weights to more important attributes. Extensive experiments are carried out on more than 18,000 projects across three repositories. The results show that our approach achieves significant improvements by using weighted combination, and the overall precision, recall and F-Measure can reach 71.41%, 65.60% and 68.38% in appropriate settings. Compared to the previous work, our approach presents competitive results with 123 finer-grained and multi-layered categories. In contrast to those using source code or byte code, our approach is more effective for large-scale and language-independent software categorization.

关键词： Databases Collaboration Servers Software maintenance Internet Data mining

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SPICE modeling of memristors with multilevel resistance states

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Chinese Physics B 2012年第9期21卷 594-600页

作者：方旭东唐玉华吴俊杰 National Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology Department of Computer Science and Technology School of ComputerNational University of Defense Technology

With CMOS technologies approaching the scaling ceiling, novel memory technologies have thrived in recent years, among which the memristor is a rather promising candidate for future resistive memory （RRAM）. Memristor＇s potential to store multiple bits of information as different resistance levels allows its application in multilevel cell （MCL） tech- nology, which can significantly increase the memory capacity. However, most existing memristor models are built for binary or continuous memristance switching. In this paper, we propose the simulation program with integrated circuits emphasis （SPICE） modeling of charge-controlled and flux-controlled memristors with multilevel resistance states based on the memristance versus state map. In our model, the memristance switches abruptly between neighboring resistance states. The proposed model allows users to easily set the number of the resistance levels as parameters, and provides the predictability of resistance switching time if the input current/voltage waveform is given. The functionality of our models has been validated in HSPICE. The models can be used in multilevel RRAM modeling as well as in artificial neural network simulations.

关键词： memristor multilevel cell SPICE model

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Latency-Aware Dynamic Voltage and Frequency Scaling on Many-Core Architectures for Data-Intensive Applications

Latency-Aware Dynamic Voltage and Frequency Scaling on Many-...

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International Conference on Cloud Computing and Big Data (CloudCom-Asia)

作者： Zhiquan Lai King Tin Lam Cho-Li Wang Jinshu Su Youliang Yan Wangbin Zhu National University of Defense Technology China The University of Hong Kong Hong Kong National Key Laboratory of Parallel and Distributed Processing (PDL) China Huawei Technologies Co. Ltd. Shenzhen China

Low power is the first-class design requirement for HPC systems. Dynamic voltage and frequency scaling (DVFS) has become the commonly used and efficient technology to achieve a trade-off between power consumption and system performance. However, most the prior work using DVFS did not take into account the latency of voltage/frequency scaling, which is a critical factor in real hardware determining the power efficiency of the power management algorithm. This paper, firstly, investigate the latency features of DVFS on a real many-core hardware platform. Secondly, we propose a latency-aware DVFS algorithm for profile-based power management to avoid aggressive power state transitions. At last, we evaluate our algorithm on Intel SCC platform using a data-intensive benchmark, Graph 500 benchmark. The experimental results not only show impressive potential for energy saving in data-intensive applications (up to 31% energy saving and 60% EDP reduction), but also evaluate the efficiency of our latency-aware DVFS algorithm which achieves 12.0% extra energy saving and 5.0% extra EDP reduction, moreover, increases the execution performance by 22.4%.

关键词： Benchmark testing Runtime Hardware Heuristic algorithms Time-frequency analysis Voltage measurement

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A fast successive over-relaxation algorithm for force-directed network graph drawing

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science China(Information sciences) 2012年第3期55卷 677-688页

作者： WANG YongXian & WANG ZhengHua National key laboratory for parallel and distributed processing, National University of Defense technology, Changsha 410073, China 1. National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Force-directed approach is one of the most widely used methods in graph drawing research. There are two main problems with the traditional force-directed algorithms. First, there is no mature theory to ensure the convergence of iteration sequence used in the algorithm and further, it is hard to estimate the rate of convergence even if the convergence is satisfied. Second, the running time cost is increased intolerablely in drawing largescale graphs, and therefore the advantages of the force-directed approach are limited in practice. This paper is focused on these problems and presents a sufficient condition for ensuring the convergence of iterations. We then develop a practical heuristic algorithm for speeding up the iteration in force-directed approach using a successive over-relaxation (SOR) strategy. The results of computational tests on the several benchmark graph datasets used widely in graph drawing research show that our algorithm can dramatically improve the performance of force-directed approach by decreasing both the number of iterations and running time, and is 1.5 times faster than the latter on average.

关键词： graph drawing graph layout successive over-relaxation force-directed algorithm

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