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检索条件"机构=Laboratory for Parallel and Distributed Computing"

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MatrixMap: Programming Abstraction and Implementation of Matrix Computation for Big Data Applications

MatrixMap: Programming Abstraction and Implementation of Mat...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yaguang Huangfu Jiannong Cao Hongliang Lu Guanqing Liang Department of Computing The Hong Kong Polytechnic University Parallel and Distributed Processing Laboratory National University of Defense Technology

The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel system for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed data structure called bulk key matrix and a computation interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with optimized data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.

关键词： Data structures Programming Linear algebra Machine learning algorithms Sparks Sparse matrices Computational modeling

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parallel image sharpness measure for supercomputing environment

Parallel image sharpness measure for supercomputing environm...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Gergely Windisch Miklós Kozlovszky Óbuda University/John von Neumann Faculty of Informatics Budapest Hungary MTA SZT AKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

Image sharpness is an important part of the overall quality of an image. There are multiple metrics to evaluate image sharpness, the algorithms are mainly created for regular desktop computers. This article describes the sharpness metrics devised and implemented at Obuda University parallelized and ported to an HPC infrastructure. Along with the native ported application a workflow has been created on the gUSE/WS-PGRADE portal system and an ASM portlet has been developed to make the service readily available to.

关键词： Measurement Supercomputers Portals Logic gates Program processors Bioinformatics Biomedical imaging

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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Situation and location awareness in harsh environment

Situation and location awareness in harsh environment

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Proceedings of the International Convention MIPRO

作者： Miklos Kozlovszky Daniela Zavec Pavlinić Andreja Oder Gábor Fehér Pál Bogdanov MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Titera Technical Innovative Technologies Ltd. Slovenia Prevent&Deloza Ltd. Celje Slovenia OMT-LAB Budapest University Hungary

Enhanced environment awareness is a key element of life protection in dangerous situations. Thus the Personal Protection Equipment (PPE) clothing industry is forced in the direction to develop highly innovative “intelligent” products, and equip their products with sensors and actuators combined with smart technologies. Besides basic functions of protection, thermal and ergonomic comfort, these new generation protective garments have additional functions such as monitoring and/or alarming for prevention in case of emergency situation. Such systems are costly because even if the clothing and engineering parts of the development are feasible, their combination is far from trivial. The different system parts have different requirements, and these requirements are sometimes not compatible with each other. Parameters such as cost, bulkiness, accuracy, independency, scalability, robustness are key parameters, which can cause high acceptance or usage prevention rapidly by the end-users. Our research is focusing on the seamless integration of the various subsystems to develop effective and user friendly, intelligent situation aware personal protection systems. Our solution helps to receive accurate information about user performance and activity conditions. The main goal of our work is to adapt the already existing monitoring solution and provide it to people working in harsh environment. The paper describes the results of our development with the description of the main functionalities.

关键词： Data acquisition Sensor fusion Monitoring Accuracy Data integration Data visualization

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Computation and communication efficient graph processing with distributed immutable view 14

Computation and communication efficient graph processing wit...

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23rd ACM Symposium on High-Performance parallel and distributed computing, HPDC 2014

作者： Chen, Rong Ding, Xin Wang, Peng Chen, Haibo Zang, Binyu Guan, Haibing Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450327480

Cyclops is a new vertex-oriented graph-parallel framework for writing distributed graph analytics. Unlike existing distributed graph computation models, Cyclops retains simplicity and computation-efficiency by synchronously computing over a distributed immutable view, which grants a vertex with read-only access to all its neighboring vertices. The view is provided via readonly replication of vertices for edges spanning machines during a graph cut. Cyclops follows a centralized computation model by assigning a master vertex to update and propagate the value to its replicas unidirectionally in each iteration, which can significantly reduce messages and avoid contention on replicas. Being aware of the pervasively available multicore-based clusters, Cyclops is further extended with a hierarchical processing model, which aggregates messages and replicas in a single multicore machine and transparently decomposes each worker into multiple threads ondemand for different stages of computation. We have implemented Cyclops based on an open-source Pregel clone called Hama. Our evaluation using a set of graph algorithms on an in-house multicore cluster shows that Cyclops outperforms Hama from 2.06X to 8.69X and 5.95X to 23.04X using hash-based and Metis partition algorithms accordingly, due to the elimination of contention on messages and hierarchical optimization for the multicore-based clusters. Cyclops (written in Java) also has comparable performance with PowerGraph (written in C++) despite the language difference, due to the significantly lower number of messages and avoided contention. Copyright © 2014 ACM.

关键词： Iterative methods

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Concurrent and consistent virtual machine introspection with hardware transactional memory

Concurrent and consistent virtual machine introspection with...

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20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014

作者： Liu, Yutao Xia, Yubin Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781479930975

Virtual machine introspection, which provides tamperresistant, high-fidelity 'out of the box' monitoring of virtual machines, has many prominent security applications including VM-based intrusion detection, malware analysis and memory forensic analysis. However, prior approaches are either intrusive in stopping the world to avoid race conditions between introspection tools and the guest VM, or providing no guarantee of getting a consistent state of the guest VM. Further, there is currently no effective means for timely examining the VM states in question. In this paper, we propose a novel approach, called TxIntro, which retrofits hardware transactional memory (HTM) for concurrent, timely and consistent introspection of guest VMs. Specifically, TxIntro leverages the strong atomicity of HTM to actively monitor updates to critical kernel data structures. Then TxIntro can mount introspection to timely detect malicious tampering. To avoid fetching inconsistent kernel states for introspection, TxIntro uses HTM to add related synchronization states into the read set of the monitoring core and thus can easily detect potential inflight concurrent kernel updates. We have implemented and evaluated TxIntro based on Xen VMM on a commodity Intel Haswell machine that provides restricted transactional memory (RTM) support. To demonstrate the effectiveness of TxIntro, we implemented a set of kernel rootkit detectors using TxIntro. Evaluation results show that TxIntro is effective in detecting these rootkits, and is efficient in adding negligible performance overhead. © 2014 IEEE.

关键词： Malware

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Bipartite-oriented distributed graph partitioning for big learning 14

Bipartite-oriented distributed graph partitioning for big le...

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5th ACM Asia-Pacific Workshop on Systems, APSYS 2014

作者： Chen, Rong Shi, Jiaxin Zang, Binyu Guan, Haibing Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450330244

Many machine learning and data mining (MLDM) problems like recommendation, topic modeling and medical diagnosis can be modeled as computing on bipartite graphs. However, most distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually causes excessive replication of vertices as well as significant pressure on network communication. This article identifies the challenges and opportunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X (from 1.38X) for four typical MLDM algorithms, due to reducing up to 62% vertex replication, and up to 96% network traffic. © 2014 ACM.

关键词： Data mining

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PUF-based secure checker protocol for Networked RFID Systems

PUF-based secure checker protocol for Networked RFID Systems

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IEEE Conference on Open Systems (ICOS)

作者： Biplob Ray Morshed Chowdhury Jemal Abawaiy Parallel and Distributed Computing Laboratory Deakin University Melbourne Australia Deakin University Burwood VIC AU

Radio Frequency Identification (RFID) system is an emerging technology for automating object identification. The Networked RFID System (NRS) is a component of a distributed object identification network which facilitates automated supply chain management. It also makes the Internet of Things (IoT) concept a reality. To increase the business feasibility of NRS implementation, the system should be able to ensure the visibility and traceability of the object throughout the chain using a checker protocol. By doing so, the protocol will check the genuineness of the object and the genuineness of the object's previous travel path on-site. While doing so, the protocol needs to ensure the security requirement of the system. To this end, we propose a secure checker protocol for NRS which will use a PUF (Physically Unclonable Function) and simple cryptographic primitives. The protocol provides security (protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability), visibility and traceability for NRS. It is also suitable for passive tags.

关键词： Protocols Radiofrequency identification Security Privacy Supply chains Equations

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Density estimation with adaptive sparse grids for large data sets 14

Density estimation with adaptive sparse grids for large data...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Peherstorfer, Benjamin Pflüger, Dirk Bungartz, Hans-Joachim Aerospace Computational Design Laboratory AeroAstro Massachusetts Institute of Technology United States Simulation of Large Systems Group Institute for Parallel and Distributed Systems University of Stuttgart Germany Scientific Computing Group Department of Computer Science Technische Universität München Germany

ISBN: (纸本)9781510811515

Nonparametric density estimation is a fundamental problem of statistics and data mining. Even though kernel density estimation is the most widely used method, its performance highly depends on the choice of the kernel bandwidth, and it can become computationally expensive for large data sets. WTe present an adaptive sparse-grid-based density estimation method which discretizes the estimated density function on basis functions centered at grid points rather than on kernels centered at the data points. Thus, the costs of evaluating the estimated density function are independent from the number of data points. We give details on how to estimate density functions on sparse grids and develop a cross validation technique for the parameter selection. We show numerical results to confirm that our sparse-grid-based method is well-suited for large data sets, and, finally, employ our method for the classification of astronomical objects to demonstrate that it is competitive to current kernel-based density estimation approaches with respect to classification accuracy and runtime. Copyright © SIAM.

关键词： Data mining

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Children obesity treatment support with telemedicine

Children obesity treatment support with telemedicine

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Proceedings of the International Convention MIPRO

作者： M. Kozlovszky K. Karóczkai B. Jókai B. Ruda D. Kacsuk Zs. Meixner John von Neumann Faculty of Informatics Óbuda University Budapest Hungary MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

Obesity is considered an endemic within the whole European continent, just as in most part of the developed world. Obesity poses an increasing risk, childhood obesity has more than doubled in children and tripled in adolescents in the past 30 years. To assess the impact of obesity we need to take into account the severity of the problems that obesity might cause, as risk is usually interpreted as the product of probability of occurrence and severity. In the last few decades, increased occurrence of several morbidities was casually linked to obesity. Recently health problems targeting children have increasing trends in well developed countries. Our research is focusing on obesity related health problems in children, and our offered ICT solutions enables healthcare service providers to remotely monitor patient's (children's) activities and weight. The solution also helps to assign workout programs to the children according to the collected sensor data. The used workout programs base not only on widely used sport activities (etc. running or gymnastics), but cover non-conventional sport activities like playing with interactive game consoles at home. The system contains an Android based DAQ (Data Acquisition Device), sensors and a server side portal, which collects all measured patient information in the data center. The solution combines existing; self developed client and server side applications and frameworks, and commodity sensor solutions. The main goal of our work is to adapt the already existing monitoring solution to children. Kids behave and use such monitoring environments differently due to their different perception and knowledge level. They need different GUIs; they live different lifestyle as elderly patients. The communication lines of the system are compliant to the EU and national regulations. The developed system was tested and a prototype system was installed successfully in a real environment. Sensor data acquisition on the personal mobile device ena

关键词： Obesity Monitoring Games Data acquisition Pediatrics Training Servers

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