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Strategies for Protecting Intellectual Property when Using CUDA applications on Graphics processing Units 16

Strategies for Protecting Intellectual Property when Using C...

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9th international conference on Security of Information and Networks (SIN)

作者： Bellekens, Xavier Paul, Greig Tachtatzis, Christos Irvine, James Atkinson, Robert Abertay Univ Dundee Scotland Univ Strathclyde Glasgow Lanark Scotland

ISBN: (纸本)9781450347648

Recent advances in the massively parallel computational abilities of graphical processing units (GPUs) have increased their use for general purpose computation, as companies look to take advantage of big data processing techniques. This has given rise to the potential for malicious software targeting GPUs, which is of interest to forensic investigators examining the operation of software. The ability to carry out reverse-engineering of software is of great importance within the security and forensics fields, particularly when investigating malicious software or carrying out forensic analysis following a successful security breach. Due to the complexity of the Nvidia CUDA (Compute Unified Device Architecture) framework, it is not clear how best to approach the reverse engineering of a piece of CUDA software. We carry out a review of the different binary output formats which may be encountered from the CUDA compiler, and their implications on reverse engineering. We then demonstrate the process of carrying out disassembly of an example CUDA application, to establish the various techniques available to forensic investigators carrying out black-box disassembly and reverse engineering of CUDA binaries. We show that the Nvidia compiler, using default settings, leaks useful information. Finally, we demonstrate techniques to better protect intellectual property in CUDA algorithm implementations from reverse engineering.

关键词： Reverse engineering Intellectual Property CUDA

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Proceedings of the 42nd international conference on Very Large Data Bases, VLDB June 2016

Proceedings of the 42nd International Conference on Very Lar...

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42nd international conference on Very Large Data Bases, VLDB 2016

This proceedings contains 11 papers. The VLDB 2016 conference covers issues in data management, database and information systems research, since they are the technological cornerstones of the emerging applications. Topics in this conference include: distributed Data Platforms, including Cloud Data systems, Key Value stores, Big Data systems;Transaction processing, Query processing, Indexing and Storage, Meta-data management, Self-Tuning technology;Specialized Data Architectures including Data Streams and IOT architectures, Cold Storage, and Hardware-Software Co-design;Data Cleaning, Data Analytics, and Data Integration;Information Retrieval and Data Mining;applications including Social Networks, Crowdsourcing, and E-commerce systems;User and Application Programming Interfaces to Data Platforms, including keyword queries, declarative and imperative languages;Benchmarking, Tuning, and Testing, etc. It also provides definitive evaluations of an established corpus of solutions to industrial-strength problems [Experiments and Analyses], which also focuses in the principled empirical evaluation of key data systems and their components, including that of specific database algorithms, and describes novel systems and applications [Innovative Systems and applications]. The key terms of this proceedings include timestamps, multi-tenant parallel databases, WarpLDA, functional dependencies and keys, Rewired User-space Memory Access (RUMA), WiSeDB, RDF querying, syntactic string transformations, method for error-tolerant autocompletion, multi-tenant workloads.

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Educational Data Mining with Python and Apache Spark: A Hands-on Tutorial 16

Educational Data Mining with Python and Apache Spark: A Hand...

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6th international conference on Learning Analytics and Knowledge (LAK)

作者： Agnihotri, Lalitha Mojarad, Shirin Lewkow, Nicholas Essa, Alfred McGraw Hill Educ 281 Summer St Boston MA 02210 USA

ISBN: (纸本)9781450341905

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.

关键词： educational data mining learning analytics python spark predictive analytics machine learning exploratory data analysis big data data mining visualization simulation parallel computing

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parallel Meshing for Finite Element Analysis 6th

Parallel Meshing for Finite Element Analysis

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6th international conference on Supercomputing in Mexico (ISUM)

作者： Cardoso, Victor E. Botello, Salvador Ctr Invest Matemat AC CIMAT Jalisco S-N Valenciana 36240 Guanajuato Mexico

ISBN: (纸本)9783319322438;9783319322421

Finite Element (FE) analysis is a well-established method to solve engineering problems, some of them require fine grained precision and, by consequence, huge meshes. A common bottle-neck in FE calculations is domain meshing. In this paper we discuss our implementation of a parallel-meshing tool. Firstly, we create a rough mesh with a serial procedure based on a Constrained Delaunay Triangulation;secondly, such a mesh is divided into N parts via spectral-bisection, where N is the number of available threads;and finally, the N parts are refined simultaneously by independent threads using Delaunay-refinement. Other proposals that use a thread to refine each part, need a user-defined subdivision. This approach calculates such a subdivision automatically while reducing the thread-communication overhead. Some researchers propose similar schemes using orthogonal-trees to create regular meshes in parallel, without any guaranty about element quality, while the Delaunay techniques have nice quality properties already proven [1-3]. Although this implementation uses a shared-memory scheme, it could be adapted in a distributed-memory strategy.

关键词： Finite element method

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Provenance: On and Behind the Screens 16

Provenance: On and Behind the Screens

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ACM SIGMOD international conference on Management of Data

作者： Herschel, Melanie Hlawatsch, Marcel Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Univ Stuttgart Visualizat Res Ctr Stuttgart Germany

ISBN: (纸本)9781450335317

Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.

关键词： data provenance tutorial provenance visualization workflow provenance

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Providing serializability for pregel-like graph processing systems 19

Providing serializability for pregel-like graph processing s...

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19th international conference on Extending Database Technology, EDBT 2016

作者： Han, Minyang Daudjee, Khuzaima David R. Cheriton School of Computer Science University of Waterloo Canada

ISBN: (纸本)9783893180707

There is considerable interest in the design and development of distributed systems that can execute algorithms to process large graphs. Serializability guarantees that parallel executions of a graph algorithm produce the same results as some serial execution of that algorithm. Serializability is required by many graph algorithms for accuracy, correctness, or termination but existing graph processing systems either do not provide serializability or cannot provide it efficiently. To address this deficiency, we provide a complete solution that can be implemented on top of existing graph processing systems. Our solution formalizes the notion of serializability and the conditions under which it can be provided for graph processing systems. We propose a novel partitionbased synchronization approach that enforces these conditions to efficiently provide serializability. We implement our partition-based technique into the open source graph processing system Giraph and demonstrate that our technique is configurable, transparent to algorithm developers, and provides large across-the-board performance gains of up to 26× over existing techniques. © 2016, Copyright is with the authors.

关键词： Open systems

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Efficient Matricization of n-D Array with CUDA and Its Evaluation 19

Efficient Matricization of n-D Array with CUDA and Its Evalu...

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19th IEEE international conference on Computational Science and Engineering, 14th IEEE international conference on Embedded and Ubiquitous Computing and 15th international Symposium on distributed Computing and applications to Business, Engineering and Science, CSE-EUC-DCABES 2016

作者： Shaikh, Md Abu Hanif Hasan, K.M. Azharul Ali, G.G. Md. Nawaz Chafii, Marwa Chong, Peter Han Joo Computer Science and Engineering Khulna University of Engineering and Technology Bangladesh School of Electrical and Electronic Engineering Nanyang Technological University Singapore CentraleSupélec IETR Campus de Rennes Cesson-Sévigné-Cedex35576 France Department of Electrical and Electronic Engineering Auckland University of Technology New Zealand

ISBN: (纸本)9781509035939

Scientific and engineering computing requires operation on flooded amount of data having very high number of dimensions. Traditional multidimensional array is widely popular for implementing higher dimensional data but its' performance diminishes with the increase of the number of dimensions. On the other side, traditional row-column view is facile for implementation, imagination and visualization. This paper details a representation scheme for higher dimensional array with row-column abstraction on parallel environment. Odd dimensions contribute along row-direction and even dimensions along column direction which gives lower cost of index computation, higher data locality and parallelism. Each 2-D block of size blockIdx.x × threadIdx.x is independent of each other. Theoretically, it has no limitation with the number of dimensions and mapping algorithm is unique for any number of dimensions. Performance of the proposed matricization is measured with matrix-matrix addition, subtraction and multiplication operation. Experimental results show promising performance improvement over Traditional Multidimensional Array (TMA) and Extended Karnaugh Map Representation (EKMR). Thus the scheme can be used for implementing higher dimensional array in both general purpose and scientific computing on GPU. © 2016 IEEE.

关键词： Graphics processing unit

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Analysis of Android malwares and their detection techniques

Analysis of Android malwares and their detection techniques

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Vinisha Malik Naveen Malik Sandip Kumar Goyal CSE Department M.M University Mullana Ambala India

ISBN: (纸本)9781509036707

Smart phones, concretely Android predicated, have magnetized the utilizers faction due to their trait affluent apps to utilize with sundry applications such as communication on social media, browsing on internet, sending and receiving emails, photo editing and video processing. However the vogue of these contrivances magnetized the malevolent assailants as well. Statistics have unveiled that Android predicated keenly intellective phones are vulnerably susceptible to malwares to a greater extent than other astute phones. This paper analyses various malware attacks posing threats to Android phones and their detection techniques along with their shortcomings and future scope of advancement in the direction.

关键词： Decision support systems Smart phones Androids Humanoid robots Mobile communication Spyware

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Simulation of cloud computation MPI applications 7

Simulation of cloud computation MPI applications

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7th international conference distributed Computing and Gridtechnologies in Science and Education, GRID 2016

作者： Kutovskiy, N.A. Nechaevskiy, A.V. Ososkov, G.A. Trofimov, V.V. Joint Institute for Nuclear Research 6 Joliot-Cur ie street Dubna Moscow Region141980 Russia Plekhanov Russian University of Economics 36 Stremyanny per. Moscow117997 Russia

At the Laboratory of Information Technologies of the Joint Institute for Nuclear Research new software for job parallel calculation on the cloud resources and computing cluster is in progress. It is expected that created cloud center of parallel computing will significantly improve the efficiency of the numerical calculations and expedite the receipt of new physically meaningful results. At the moment, on the basis of the JINR computer center a parallel algorithm and corresponding software for parallel computing using MPI technology are developed. To optimize the scheme of parallel computations it is necessary to test the algorithm for various combinations of equipment parameters, number of processors and parallelization levels. Thus, the need to evaluate the impact of various factors (processor speed, throughput of communication network, its latency) to computing speed of particular job is arisen. Such evaluation can be achieved by simulating the MPI computing processes. Results of the MPI computing simulations is presented on the example of calculating long Josephson junctions. © 2016 Nikolay A. Kutovskiy, Andrey V. Nechaevskiy, Gennadiy A. Ososkov, Vladimir V. Trofimov.

关键词： parallel processing systems

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Domain Segmentation of Meshes Suited for FEM Using parallel Computing 6th

Domain Segmentation of Meshes Suited for FEM Using Parallel ...

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6th international conference on Supercomputing in Mexico (ISUM)

作者： Lopez, Jorge Cardoso, Victor E. Botello, Salvador Ctr Invest Matemat AC Dept Ciencias Computac Jalisco S-N Guanajuato 36240 Gto Mexico

ISBN: (纸本)9783319322438;9783319322421

The Finite Element Method is widely used in the industry, how is mentioned in [4]. In this method most of the analysis require huge meshes to discretize the geometry into finite elements. Such meshes are processed slowly in a single modern computer due to the limits on memory and processing units. The problem is tackled by dividing the mesh into several sub-meshes with an algorithm similar to the mentioned in [2], this procedure is known as domain segmentation and it is considered a complex problem by itself, because the segmentation requires to maintain a balanced number of nodes for each sub-domain, while minimizing the number of edges in the boundaries of such domains like in [3]. This is made in order to decrease the intercommunication of process when solving FEM problems in a distributed memory scheme. In this work we parallelize the spectral bisection algorithm proposed in [1]. The output of this algorithm could be used for two purposes, (1) to segment the domain and (2) to enumerate the nodes of the mesh in order to reduce the fill-in of the LU decomposition, this enumeration is also know as labelling.

关键词： parallel computing Domain segmentation Labelling FEM Spectral bisection

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