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检索条件"主题词=PARALLEL algorithms"

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parallelized Side-Channel Attack Resisted Scalar Multiplication Using q-Based Addition-Subtraction k-chains 4

Parallelized Side-Channel Attack Resisted Scalar Multiplicat...

引用

4th International Symposium on Computing and Networking (CANDAR)

作者： Phalakarn, Kittiphop Phalakarn, Kittiphon Suppakitpaisarn, Vorapong Chulalongkorn Univ Dept Comp Engn Bangkok Thailand Univ Tokyo Dept Comp Sci Tokyo Japan

ISBN: (纸本)9781509026555

This paper presents parallel scalar multiplication techniques for elliptic curve cryptography using q-based addition-subtraction k-chain which can also effectively resist side-channel attack. Many techniques have been discussed to improve scalar multiplication, for example, double-and-add, NAF, w-NAF, addition chain and addition-subtraction chain. However, these techniques cannot resist side-channel attack. Montgomery ladder, random w-NAF and uniform operation techniques are also widely used to prevent side-channel attack, but their operations are not efficient enough comparing to those with no side-channel attack prevention. We have found a new way to use k-chain for this purpose. In this paper, we extend the definition of k-chain to q-based addition-subtraction k-chain and modify an algorithm proposed by Jarvinen et al. to generate the q-based addition-subtraction k-chain. We show the upper and lower bounds of its length which lead to the computation time using the new chain techniques. The chain techniques are used to reduce the cost of scalar multiplication in parallel ways. Comparing to wNAF, which is faster than double-and-add and Montgomery ladder technique, the maximum computation time of our q-based addition-subtraction k-chain techniques can have up to 25.92% less addition costs using only 3 parallel computing cores. We also discuss on the optimization for multiple operand point addition using hybrid-double multiplier which is proposed by Azarderakhsh and Reyhani-Masoleh. The proposed parallel chain techniques can also tolerate side-channel attack efficiently.

关键词： Information and Communication Security Efficient Implementations parallel algorithms Elliptic Curve Cryptography Scalar Multiplication k-Chain Side-Channel Attack Countermeasure

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Optimizing Indirect Memory References with milk 16

Optimizing Indirect Memory References with milk

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International Conference on parallel Architectures and Compilation (PACT)

作者： Kiriansky, Vladimir Zhang, Yunming Amarasinghe, Saman MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450341219

Modern applications such as graph and data analytics, when operating on real world data, have working sets much larger than cache capacity and are bottlenecked by DRAM. To make matters worse, DRAM bandwidth is increasing much slower than per CPU core count, while DRAM latency has been virtually stagnant. parallel applications that are bound by memory bandwidth fail to scale, while applications bound by memory latency draw a small fraction of much-needed bandwidth. While expert programmers may be able to tune important applications by hand through heroic effort, traditional compiler cache optimizations have not been sufficiently aggressive to overcome the growing DRAM gap. In this paper, we introduce milk a C/C++ language extension that allows programmers to annotate memory bound loops concisely. Using optimized intermediate data structures, random indirect memory references are transformed into batches of efficient sequential DRAM accesses. A simple semantic model enhances programmer productivity for efficient parallelization with OpenMP. We evaluate the MILK compiler on parallel implementations of traditional graph applications, demonstrating performance gains of up to 3 x

关键词： bsp parallel algorithms irregular memory access indirect memory reference graph algorithms milk openmp

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Solving Multidimensional Global Optimization Problems Using Graphics Accelerators 2nd

Solving Multidimensional Global Optimization Problems Using ...

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2nd Russian Supercomputing Days Conference (RuSCDays)

作者： Barkalov, Konstantin Lebedev, Ilya Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod Russia

ISBN: (纸本)9783319556680;9783319556697

In the present paper an approach to solving the global optimization problems using a nested optimization scheme is developed. The use of different algorithms at different nesting levels is the novel element. A complex serial algorithm (on CPU) is used at the upper level, and a simple parallel algorithm (on GPU) is used at the lower level. This computational scheme has been implemented in ExaMin parallel solver. The results of computational experiments demonstrating the speedup when solving a series of test problems are presented.

关键词： Global optimization Multiextremal functions Dimension reduction parallel algorithms Speedup Graphics accelerators

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Study of Particle Swarm Optimization algorithms Using Message Passing Interface and Graphical Processing Units Employing a High Performance Computing Cluster 6th

Study of Particle Swarm Optimization Algorithms Using Messag...

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6th International Conference on Supercomputing in Mexico (ISUM)

作者： Santana-Castolo, Manuel-H. Alejandro Morales, J. Torres-Ramos, Sulema Alanis, Alma Y. Univ Guadalajara CUCEI Apartado Postal 51-71 Zapopan 45080 Jalisco Mexico

ISBN: (纸本)9783319322438;9783319322421

Particle Swarm Optimization (PSO) is a heuristic technique that have been used to solve problems where many events occur simultaneously and small pieces of the problem can collaborate to reach a solution. Among its advantages are fast convergence, large exploration coverage, and adequate global optimization;however to address the premature convergence problem, modifications to the basic model have been developed such as Aging Leader and Challengers (ALC) PSO and Bio-inspired Aging (BAM) PSO. Being these algorithms parallel in nature, some authors have attempted different approaches to apply PSO using MPI and GPU. Nevertheless ALC-PSO and BAM-PSO have not been implemented in parallel. For this study, we develop PSO, ALC-PSO and BAM-PSO, through MPI and GPU using the High Performance Computing Cluster (HPCC) Agave. The results suggest that ALC-PSO and BAM-PSO reduce the premature convergence, improving global precision, whilst BAM-PSO achieves better optimal at the expense of significantly increasing the algorithm computational complexity.

关键词： ALC-PSO BAM-PSO CUDA GPUs HPCC MPI parallel algorithms Particle swarm optimization PSO

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ITERATIVE SOLVERS FOR MESHLESS PETROV GALERKIN (MLPG) METHOD APPLIED TO LARGE SCALE ENGINEERING PROBLEMS CHALLENGES

ITERATIVE SOLVERS FOR MESHLESS PETROV GALERKIN (MLPG) METHOD...

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ASME International Mechanical Engineering Congress and Exposition (IMECE2015)

作者： Singh, Rituraj Singh, Krishna M. Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India

ISBN: (纸本)9780791857496

In recent years, significant research effort has been invested in development of mesh-free methods for different types of continuum problems. Prominent amongst these methods are element free Galerkin (EFG) method, RKPM, and mesh-less Petrov Galerkin (MLPG) method. Most of these methods employ a set of nodes for disbretization of the problem domain, and use a moving least squares (MLS) approximation to generate shape functions. Of these methods, MLPG method is seen as a pure meshless method since it does not require any background mesh. Accuracy and flexibility of MLPG method is well established for a variety of continuum problems. However, most of the applications have been limited to small scale problems solvable on serial machines. Very few attempts have been made to apply it to large scale problems which typically involve many millions (or even billions) of nodes and would require use of parallel algorithms based on domain decomposition. Such parallel techniques are well established in context of mesh-based methods. Extension of these algorithms in conjunction with MLPG method requires considerable further research. Objective of this paper is to spell out these challenges which need urgent attention to enable the application of meshless methods to large scale problems. We specifically address the issue of the solution of large scale linear problems which would necessarily require use of iterative solvers. We focus on application of BiCGSTAB method and an appropriate set of preconditioners for the solution of the MLPG system.

关键词： MLPG meshless methods large scale problems parallel algorithms domain decomposition

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A Scheme of Independent Calculations in a Precedence Constrained Routing Problem 9th

A Scheme of Independent Calculations in a Precedence Constra...

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9th International Conference on Discrete Optimization and Operations Research (DOOR)

作者： Chentsov, Alexander G. Grigoryev, Alexey M. Krasovskii Inst Math & Mech Ekaterinburg Russia Ural Fed Univ Ekaterinburg Russia

ISBN: (纸本)9783319449142;9783319449135

We consider a routing problem with constraints. To solve this problem, we employ a variant of the dynamic programming method, where the significant part (that is, the part that matters in view of precedence constraints) of the Bellman function is calculated by means of an independent calculations scheme. We propose a parallel implementation of the algorithm for a supercomputer, where the construction of position space layers for the hypothetical processors is conducted with use of discrete dynamic systems' apparatus.

关键词： Dynamic programming Routing problem Precedence constraints Sequential ordering problem parallel algorithms

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Out-of-core GPU accelerated surface reconstruction for large industrial environment monitoring 4

Out-of-core GPU accelerated surface reconstruction for large...

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4th International Conference on Applied Robotics for the Power Industry (CARPI)

作者： Miralles, Francois Xu, Chen Laurendeau, Denis IREQ Hydroquebec Res Inst Varennes PQ Canada Univ Laval Dept Elect Engn Quebec City PQ Canada

ISBN: (纸本)9781509032280

A parallel implementation of a surface reconstruction algorithm is presented. This algorithm uses the vector field surface representation and was adapted in a previous work by the authors to handle large scale environment reconstruction. Two parallel implementations with different memory requirements and processing speeds are described and compared. These parallel implementations increase the vector field computation speed by a factor of up to 31 times relative to a purely serial implementation. The method is demonstrated on different datasets captured on the sites of Hydro-Quebec using a variety of sensors: LiDAR, sonar and the WireScan, an underwater laser scanner designed at our laboratory.

关键词： Computer vision parallel algorithms

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Jaccard Coefficients as a Potential Graph Benchmark 30

Jaccard Coefficients as a Potential Graph Benchmark

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30th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Kogge, Peter M. Univ Notre Dame CSE Dept Notre Dame IN 46556 USA

ISBN: (纸本)9781509036820

The processing of graphs is of increasing importance in many applications, with the size of such graphs growing rapidly. As with scientific computing, there is a growing need to understand the relationship between system architectures and graph algorithms, especially as both the scale of the system and the size of the graph increase. To date there is one such graph benchmark that has several hundred comparative reports available, namely Breadth First Search, which has over the last few years fueled new algorithms that have improved typical performance very significantly. This paper suggests an additional benchmark based on the computation of neighborhoods and Jaccard coefficients that is of both a different intrinsic complexity and can be recast in multiple ways that may be suitable for different classes of real-world applications.

关键词： parallel algorithms graph benchmarks Jaccard Coefficients

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Optimization of MLHL-SIM and SIM Algorithm Using OpenMP 17th

Optimization of MLHL-SIM and SIM Algorithm Using OpenMP

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17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Sanchez, Lidia Quintian, Hector Perez, Hilde Corchado, Emilio Univ Leon Dept Mech Comp & Aerosp Engn Leon Spain Univ Salamanca Dept Informat & Automat Salamanca Spain

ISBN: (纸本)9783319446363;9783319446356

In this research a parallel version of two existing algorithms that implement Maximum Likelihood Scale Invariant Map (MLHL-SIM) and Scale Invariant Map (SIM) is proposed. By using OpenMP to distribute the independent iterations of for-loops among the available threads, a significant reduction in the computation time for all the experiments is achieved. The higher the size of the considered map is, the higher the reduction of the computation time in the parallel algorithm is. So, for two given datasets, measured times are up to a 29.45% and a 36.21% of the sequential time for the MLHL-SIM algorithm. For the SIM algorithm it also reduces the computation time being a 42.09% and a 36.72% of the sequential version for the two datasets respectively. Results prove the improvement on the speed up of the parallel version.

关键词： parallel algorithms OpenMP Performance Maximum Likelihood Hebbian Learning Scale Invariant Map

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Hierarchical Density-Based Clustering based on GPU Accelerated Data Indexing Strategy

Hierarchical Density-Based Clustering based on GPU Accelerat...

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16th Annual International Conference on Computational Science (ICCS)

作者： Melo, Danilo Toledo, Savyo Mourao, Fernando Sachetto, Rafael Andrade, Guilherme Ferreira, Renato Parthasarathy, Srinivasan Rocha, Leonardo Univ Fed Sao Joao Del Rei Sao Joao Del Rei MG Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

Due the recent increase of the volume of data that has been generated, organizing this data has become one of the biggest problems in Computer Science. Among the different strategies propose to deal efficiently and effectively for this purpose, we highlight those related to clustering, more specifically, density-based clustering strategies, which stands out for its ability to define clusters of arbitrary shape and the robustness to deal with the presence of data noise, such as DBSCAN and OPTICS. However, these algorithms are still a computational challenge since they are distance-based proposals. In this work we present a new approach to make OPTICS feasible based on data indexing strategy. Although the simplicity with which the data are indexed, using graphs, it allows explore various parallelization opportunities, which were explored using graphic processing unit (GPU). Based on this structure, the complexity of OPTICS is reduced to O(E * logV) in the worst case, becoming itself very fast. In our evaluation we show that our proposal can be over 200x faster than its sequential version using CPU.

关键词： Hierarchical Density Clustering parallel algorithms Graphic Processing Unit

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