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检索条件"主题词=Algorithm Design And Analysis"

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Providing Differentiated Services, Congestion Management, and Deadlock Freedom in Dragonfly Networks

Providing Differentiated Services, Congestion Management, an...

引用

IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB)

作者： Pedro Yébenes Jesus Escudero-Sahuquillo Pedro J. García Francisco J. Alfaro Francisco J. Quiles Universidad de Castilla-La Mancha Ciudad Real Castilla-La Mancha ES

High-Performance Computing (HPC) systems and Datacenters are growing in size to meet the performance required by the applications. This makes the interconnection network a central element in these systems, which must provide high-communication bandwidth and low latency. One of the most important aspects in the design of interconnection networks is their topology. In that sense, the Dragonfly topology is nowadays very popular as it offers high scalability, low diameter, diversity of paths, high bisection bandwidth, etc. Routing in Dragonfly topologies is an important issue, since deadlocks must be prevented, usually by means of escape ways to break cycles. However, even with a deadlock-free routing, the performance of Dragonfly topologies degrades when the negative effects of congestion appear (e.g. the Head-of-Line-HoL-blocking). Hence, some techniques are used to reduce HoL blocking, usually based on separating traffic flows into different queues implemented by means of Virtual Channels (VCs) or similar structures. VCs are also used to implement differentiated-services provision in systems supporting applications with different levels of priority. The combination of HoL-blocking reduction and differentiated-services provision through an integrated management of VCs has been previously studied. However, this integrated management of VCs does not consider the additional use of VCs as escape ways, thus it cannot be applied to Dragonfly topologies. For that reason, in this paper, we adapt the integrated management of VCs to Dragonfly topologies, so that by using a reduced number of VCs, our proposal is able to offer differentiated-services provision, HoL-blocking reduction and deadlock freedom. Performance results, obtained through simulation experiments in medium-to-large size Dragonfly networks, show that the proposed technique outperforms other solutions.

关键词： Topology Routing Network topology Switches System recovery algorithm design and analysis Multiprocessor interconnection

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Fireworks algorithm: A new swarm intelligence technique for electromagnetic optimization

Fireworks algorithm: A new swarm intelligence technique for ...

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Antennas and Propagation Society International Symposium

作者： Elie BouDaher Ahmad Hoorfar Antenna Research Laboratory Center for Advanced Communications Villanova University Villanova PA USA

This paper presents the application of a new and recently reported swarm intelligence technique, named Fireworks algorithm (FWA) to electromagnetic and antenna problems. The operation of the basic principles of FWA is discussed. The FWA is then implemented in the optimization of a benchmark antenna problem: gain optimization of Yagi-Uda antennas. The performance of FWA is compared with other nature-based optimization algorithms. Preliminary results indicate the potentials of FWA in efficient optimization of electromagnetic problems.

关键词： Optimization Electromagnetics Antennas algorithm design and analysis Heuristic algorithms Particle swarm optimization Sparks

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Enhancing the Scalability and Memory Usage of Hashsieve on Multi-core CPUs

Enhancing the Scalability and Memory Usage of Hashsieve on M...

引用

Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Artur Mariano Christian Bischof Institute for Scientific Computing Technische Universität Darmstadt Darmstadt Germany

The Shortest Vector Problem (SVP) is a key problem in lattice-based cryptography and cryptanalysis. While the cryptography community has accumulated a vast knowledge of SVP-solvers from a theoretical standpoint, the practical performance of these algorithms is commonly not well understood. This gap in knowledge poses many challenges to cryptographers, who are oftentimes confronted with algorithms that perform worse in practice then expected from theory. This is a problem because the asymptotic complexity of the best algorithms plays a key role in the construction of cryptosystems, but only practically appealing, validated algorithms are accounted for in this process. Thus, if one cannot extract the full potential of theoretically strong algorithms in practice, efficient algorithms might be ruled out and wrong assumptions are made when constructing cryptosystems. In this paper, we take a step forward to fill this gap, by providing a computational analysis of HashSieve, the most practical sieving SVP-solver to date, and showing how its performance can be enhanced in practice. To this end, we revisit the parallel generation of random numbers, memory allocation and memory access patterns. Employing scalable random sampling, object memory pools, scalable memory allocators and aggressive memory prefetching, we were able to improve the best current implementation of HashSieve by factors of 3x and 4x, depending on the lattice dimension, and set new records for the HashSieve algorithm, thereby shrinking the gap between its theoretical complexity and its performance in practice.

关键词： Cryptography Lattices algorithm design and analysis Scalability Optimization Computational complexity

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GraphFlow: Workflow-based big graph processing

GraphFlow: Workflow-based big graph processing

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IEEE International Conference on Big Data

作者： Sara Riazi Boyana Norris Department of Information and Computer Science University of Oregon Eugene OR

ISBN: (纸本)9781467390064

We introduce GraphFlow, a big graph framework that is able to encode complex data science experiments as a set of high-level workflows. GraphFlow combines the Spark big data processing platform and the Galaxy workflow management system to offer a set of components for graph processing using a novel interaction model for creating and using complex workflows. GraphFlow contributes an easy-to-use interface and scalable algorithms for big graph analytics. We demonstrate GraphFlow use in large social network analysis with several case studies.

关键词： Sparks Big data algorithm design and analysis Data science Bioinformatics Parallel processing Engines

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Noise stable image registration using RANdom RESAmple Consensus

Noise stable image registration using RANdom RESAmple Consen...

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International Conference on Pattern Recognition

作者： Atsushi Nakazawa Department of Informatics Kyoto University

ISBN: (纸本)9781509048489

Image registration is an important and fundamental problem in computer vision and image processing. Although there are currently a large number of image registration algorithms such as RANSAC and its extensions, image registration under very noisy conditions remains difficult when it cannot obtain enough number of correct corresponding points. This paper solves this issue by introducing a random resample consensus (RANRESAC) strategy, which achieves robust registration where it is difficult to obtain enough numbers of correct correspondence pairs. In contrast to RANSAC, proposed RANRESAC newly generate corresponding points for the images using the hypothesis transformation function, and verifies the correctness by evaluating the similarity of the local features at the newly sampled points. To confirm the effectiveness for the proposed method, we first conducted an preliminary experiment that evaluates the similarity of texture and orientation components of SURF local descriptor in the images adding several levels of noise. As the result, we observed the texture component is more stable than the orientation component. Based on this finding, we design the RANRESAC algorithm and performed experiments using a open image registration dataset. As the result, proposed method outperforms to the RANSAC, MSAC and Optimal RANSAC algorithms in large noise conditions.

关键词： Image registration Noise measurement Feature extraction Robustness algorithm design and analysis Cameras

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A novel dual-channel joint analysis and measurement algorithm on broadband cross-channel radar radiant signal

A novel dual-channel joint analysis and measurement algorith...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yurong Wan Tao Li Zengping Chen Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781509060948

Accurate and quick parameter estimation of linear Frequency Modulation (LFM) signal is always the research hot. In this paper, an improved dual-channel joint analysis of measurement algorithm is proposed for the recognition and parameter estimation of cross-channel LFM signal. First, coarse chirp rates are estimated by delay-related correlative dechirp, and then signal is identified coarsely by a comparison of chirp rates of two channels. Second, the initial frequency and chirp rate of channel A are estimated based on the Fractional Fourier Transform (FRFT) which needs to do local search. Finally, the estimated chirp rate of channel A is utilized to identify the signal in channel B with the parameters in time and frequency domain. Simulation results show that accuracy of recognition and parameter estimation for cross-channel LFM signal is satisfying when the signal to noise rate (SNR) is not less than -16 dB.

关键词： Chirp Parameter estimation Channel estimation algorithm design and analysis Signal to noise ratio Estimation Electromagnetics

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Architecture and Implementation of a Simplified Locality Sensitive Hashed Signatures Based Fast Motion Estimation algorithm

Architecture and Implementation of a Simplified Locality Sen...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Pavel Arnaudov Tokunbo Ogunfunmi Dept. of Electr. Eng. Santa Clara Univ. Santa Clara Santa Clara CA USA Department of Electrical Engineering Santa Clara University Santa Clara CA USA

ISBN: (纸本)9781509033621

Motion estimation consumes the major part of time and power in both video compression standards - HEVC and H.264. This paper presents a Fast Motion Estimation algorithm, which targets Full Search quality even at HD resolution. It is an enhancement of existing Fast Motion Estimation algorithms with the main purpose of reducing cost and power consumption of handheld devices performing Motion Estimation, while preserving the picture quality. The proposed algorithm is based on dimensionality reduction and uses locality sensitive hash functions to achieve "Quantitative Expression of Similarity". The algorithm is an extension to other existing algorithms in the literature and especially one of the most efficient algorithms - HMDS.

关键词： Resists Motion estimation Signal processing algorithms algorithm design and analysis High definition video Heuristic algorithms Video compression

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Time-series clustering for data analysis in Smart Grid

Time-series clustering for data analysis in Smart Grid

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Akanksha Maurya Alper Sinan Akyurek Baris Aksanli Tajana Simunic Rosing Electrical and Computer Engineering Department University of California San Diego (UCSD)

ISBN: (纸本)9781509040766

The increasingly pervasive deployment of networked sensors in the Smart Grid for monitoring energy consumption has resulted in an unprecedentedly large amount of data generation. Efficient methods are required to understand this high volume and high dimensional data on an embedded platform, which has many challenges due to memory, processing and power constraints. One of the popular methods to analyze time-series data is clustering. In this paper, we discuss D-Stream II, a common time-series clustering algorithm, and demonstrate that it fails to obtain clusters in sample Smart Grid applications. Then, we propose an enhanced version of this algorithm, which handles the scenarios where the original algorithm fails. We show the effectiveness of our algorithm using real residential power consumption data from Pecan Street database. Our enhanced algorithm efficiently handles the high volume energy consumption data and captures the everyday energy consumption patterns of each residential home, which allow the consumer to compare energy consumption with their neighbors or detect any abnormality such as a defective appliance. The consumers can also be clustered into different groups, which can be effectively used to enhance the demand response policies. Our algorithm can perform clustering on approximately half a million energy consumption data points using only 5KB max memory, 360J max overhead in energy consumption and can complete in 8 mins on a resource limited embedded platform (Raspberry Pi 2).

关键词： Clustering algorithms Smart grids Heuristic algorithms algorithm design and analysis Energy consumption Partitioning algorithms Memory management

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Optimizing Modular Multiplication for NVIDIA's Maxwell GPUs

Optimizing Modular Multiplication for NVIDIA's Maxwell GPUs

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Computer Arithmetic (ARITH)

作者： Niall Emmart Justin Luitjens Charles Weems Cliff Woolley College of Information and Computer Sciences University of Massachusetts Amherst MA USA NVIDIA Corporation Santa Clara CA USA

ISBN: (纸本)9781509016174

In this paper we show how we were able to achieve record rates of multiple precision (MP) modular multiplication (mulmod) operations in the new NVIDIA MP math library (XMP) on Maxwell, NVIDIA's most recent generation of graphics processing units (GPUs). Mulmod is a key operation that is used in multiple places within the MP library, and has many real world applications, especially in cryptography, which makes it important to achieve a highly optimized implementation. Here we reveal how multiple techniques were combined to make the best use of the GPU'sinstructions, registers, memory, and threads. A particularly interesting algorithmic aspect, designed to work with the 16-bit hardware multipliers found in Maxwell, is the use of a two-pass process to first compute unaligned partial products, then shift the result 16 bits to the left, then compute the aligned partial products. The new algorithms are much faster than the prior, state of the art, row-oriented multiply and reduce approach, achieving speedups of 61% at 256 bits, and 117% at 512 bits, with peaks rates of 4027 million mulmod operations at 256 bits and 1081 million at 512 bits on a GTX 980Ti.

关键词： Graphics processing units Hardware Instruction sets Throughput algorithm design and analysis Libraries Registers

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Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism

Lipschitz Extensions for Node-Private Graph Statistics and t...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Sofya Raskhodnikova Adam Smith Computer Science and Engineering Department Pennsylvania State University University Park PA USA

ISBN: (纸本)9781509039340

Lipschitz extensions were proposed as a tool for designing differentially private algorithms for approximating graph statistics. However, efficiently computable Lipschitz extensions were known only for 1-dimensional functions (that is, functions that output a single real value). We study efficiently computable Lipschitz extensions for multi-dimensional (that is, vector-valued) functions on graphs. We show that, unlike for 1-dimensional functions, Lipschitz extensions of higher-dimensional functions on graphs do not always exist, even with a non-unit stretch. We design Lipschitz extensions with small stretch for the sorted degree list and degree distribution of a graph, viewed as functions from the space of graphs equipped with the node distance into real space equipped with l1. Our extensions are from the space of bounded-degree graphs to the space of arbitrary graphs. The extensions use convex programming and are efficiently computable. We also develop a new tool for employing Lipschitz extensions in differentially private algorithms that operate with no prior knowledge of the graph (and, in particular, no knowledge of the degree bound). Specifically, we generalize the exponential mechanism, a widely used tool in data privacy. The exponential mechanism is given a collection of score functions that map datasets to real values. It returns the name of the function with nearly minimum value on the dataset. Our generalized exponential mechanism provides better accuracy than the standard exponential mechanism when the sensitivity of an optimal score function is much smaller than the maximum sensitivity over all score functions. We use our Lipschitz extensions and the generalized exponential mechanism to design a node differentially private algorithm for approximating the degree distribution of a sensitive graph. Our algorithm is much more accurate than those from previous work. In particular, our algorithm is accurate on all graphs whose degree distributions decay at lea

关键词： Sensitivity algorithm design and analysis Approximation algorithms Privacy Data privacy Functional analysis

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