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检索条件"任意字段=Proceedings of the 5th IEEE Symposium on Parallel and Distributed Processing"

共 3536 条记录，以下是301-310 订阅

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ieee International symposium on Electromagnetic Compatibility

IEEE International Symposium on Electromagnetic Compatibilit...

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5th ieee International symposium on Electromagnetic Compatibility, EMC-Beijing 2017

ISBN: (纸本)9781509051854

the proceedings contain 152 papers. the topics discussed include: experiment and simulation analysis of BMM electrostatic discharge simulator;measurement and analysis of electromagnetic disturbance at the secondary side of electronic voltage transformer due to switching operations via a 6 Kv switchgear;mitigation of EMI in a coupled inductors-high boost DC-DC converter by programmed PWM;broadband Doherty power amplifier with modified post-matching structure;the number of chained lumped-circuits for the physical analogy of half-wavelength power transmission lines;tracing movement of thundercloud based on network-distributed lightning electric field sensors;a new method using covariance eigenvalues and time window in passive human motion detection based on CSI phases;influence of microwave pulse parameters on the burnout effect of an LNA constructed by a GaAs PHEMT;synthesis of unequally spaced array using multi-stage differential evolution algorithm;joint predistortion of IQ impairments and pa nonlinearity in concurrent tri-band transmitters with a dual-up-conversion architecture;a co-share channel for wireless power and signal transmission in short range;application analysis on the simulation of the ACL for PCB ground net in EFT test;identification of wireless transceiver devices using radio frequency (RF) fingerprinting based on STFT analysis to enhance authentication security;a novel FMCW waveform for multi-target detection and the corresponding algorithm;analysis of the characteristics and impact of lightning on smart substations;and algorithm and implementation of radio frequency fingerprinting based on multi-resolution analysis.

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Synthesis and thermal Characterization of Sodium Borosilicate/Carbon Nanofiber Composites 16

Synthesis and Thermal Characterization of Sodium Borosilicat...

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16th ieee InterSociety Conference on thermal and thermomechanical Phenomena in Electronic Systems (Itherm)

作者： Copenhaver, Katie E. Nadler, Jason H. Beckert, Michael Georgia Tech Res Inst 400 10th St NW Atlanta GA 30318 USA

ISBN: (纸本)9781509029945

Sodium borosilicate (NBS) glass/annealed pyrolytic graphite (APG) fiber composites have been fabricated through a sol-gel process. A glass composition of 80wt% SiO2, 15wt% B2O3, and 5wt% Na2O was chosen, similar to commercial NBS glasses such as Pyrex. A variety of gels with increasing weight percentages of APG were produced and subsequently heat treated and sintered in a uniaxial hot-press. Sol-gel derived NBS glass composites have been prepared with up to 40wt% of APG homogeneously distributed throughout the glassy matrix. Increased loading of the sol-gel derived sodium borosilicate glass with SiO2-coated APG fibers has shown to decrease its coefficient of thermal expansion (CTE) below that of traditional melt-quenched NBS glasses of similar composition while keeping the bulk density relatively constant. the high aspect ratio of APG fibers encourages the formation of percolating networks within the glassy matrix, increasing the thermal conductivity of the composite material beyond that of a conventional NBS glass. the resulting thermal conductivity of the composites has been found to be directional, with a much higher conductivity value in the direction perpendicular to the axis of the hot press ("in-plane") than in its parallel direction ("through-thickness"), indicating of orientation of the fiber axes perpendicular to the direction of applied pressure during processing.

关键词： sol-gel thermal conductivity nanofiber sodium borosilicate thermal expansion nanocomposite annealed pyrolytic graphite

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A New Efficient distributed Computing Middleware based on Cloud Micro-Services for HPC 5

A New Efficient Distributed Computing Middleware based on Cl...

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5th International Conference on Multimedia Computing and Systems (ICMCS)

作者： Benchara, Fatema Zahra Youssfi, Mohamed Bouattane, Omar Ouajji, Hassan Hassan II Univ Casablanca Lab SSDIA ENSET Mohammedia Mohammadia Morocco

ISBN: (纸本)9781509051465

the aim of this paper is to present a new distributed computing middleware for High Performance Computing (HPC) based cloud micro-services. the great challenge is to maintain the scalability and efficiency of massively parallel and distributed computational system when the intensive big data processed by its applications is widely increased. Besides, the proposed middleware implements a new cooperative micro-services team works model for massively parallel and distributed computing. this model is constituted by distributed micro-services as Micro-service Virtual processing Units (MsVPUs) with integrated load balancing service and an AMQP communication protocol that grant HPC. the paper shows the proposed distributed computational scheme and its integrated middleware accompanying by some experimental results.

关键词： parallel and distributed Computing Middleware Micro-services Cloud Computing Load balancing High Performance Computing

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A parallel Programming Course Based on an Execution Time-Energy Consumption Optimization Problem 30

A Parallel Programming Course Based on an Execution Time-Ene...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Cuenca, Javier Gimenez, Domingo Univ Murcia Dept Engn & Technol Comp E-30071 Murcia Spain Univ Murcia Dept Comp & Syst E-30071 Murcia Spain

ISBN: (纸本)9781509036820

this paper presents an experience of Problem-based learning in a parallel Programming course. the course includes the basics of parallel Programming, from methodological and technological aspects to the analysis and design of parallel algorithms. the students work with an optimization problem in the field of parallel Computing. the execution time and the energy consumption of a simplified master-slave scheme in a simplified heterogeneous system are optimized, so treating it as a bi-objective optimization problem, which is addressed with sequential, shared-memory, message-passing and hybrid parallel programming. In this way, the students follow the various parts of the syllabus of the course by working with a problem in which topics studied in previous courses are combined (green computing, computational systems architecture, optimization, heuristics), and this contributes to a deeper understanding of these topics and motivates the introduction of new concepts.

关键词： problem-based learning parallel programming bi-objective optimization execution time energy consumption

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Service Load Balancing in Fog-based 5G Radio Access Networks 28

Service Load Balancing in Fog-based 5G Radio Access Networks

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28th Annual ieee International symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Jijin, Jofina Seet, Boon-Chong Chong, Peter Han Joo Jarrah, Hazim Auckland Univ Technol Dept Elect & Elect Engn Auckland 1010 New Zealand

ISBN: (纸本)9781538635315

Fog-based radio access networks (F-RAN) are poised to play a significant role in future 5th generation (5G) cellular networks by harnessing the distributed resources of collaborative edge devices to deliver localized RAN services to the end-users. through ingestion and processing of end-user tasks close to their sources, F-RAN has potential to meet the stringent latency and bandwidth requirements of 5G services and applications. Due to the multitude of edge devices with different resource capacities that can be selected by the fog access point (FAP) as service nodes to process an end-user task, an important issue is deciding which service node(s) should be assigned to process what tasks from end-users in the F-RAN. the tasks here refer to user processing tasks that would have normally performed by the FAP or remotely by the cloud. In this paper, we introduce the concept of virtual FAPs (v-FAPs) formed by a number of local devices such as WiFi access points, femtocell base-stations and the more resource-rich end-user devices under the coverage and management of the FAP. this paper focuses on addressing the task assignment problem by proposing a service load balancing algorithm for the v-FAPs. We model the user tasks as a task graph and the service nodes as an edgeless service graph. We then formulate an optimization problem to find the optimal assignment of tasks with the objective of balancing loads at the service nodes.

关键词： fog computing radio access network task assignment load balancing 5G

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Testing fine-grained parallelism for the ADMM on a factor-graph 30

Testing fine-grained parallelism for the ADMM on a factor-gr...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Hao, Ning Oghbaee, AmirReza Rostami, Mohammad Derbinsky, Nate Bento, Jose

ISBN: (纸本)9781509036820

there is an ongoing effort to develop tools that apply distributed computational resources to tackle large problems or reduce the time to solve them. In this context, the Alternating Direction Method of Multipliers (ADMM) arises as a method that can exploit distributed resources like the dual ascent method and has the robustness and improved convergence of the augmented Lagrangian method. Traditional approaches to accelerate the ADMM using multiple cores are problem-specific and often require multi-core programming. By contrast, we propose a problem-independent scheme of accelerating the ADMM that does not require the user to write any parallel code. We show that this scheme, an interpretation of the ADMM as a message-passing algorithm on a factor-graph, can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control. Specifically, we obtain 10-18x speedup using a GPU, and 5-9x using multiple CPU cores, over a serial, optimized C-version of the ADMM, which is similar to the typical speedup reported for existing GPU-accelerated libraries, including cuFFT (19x), cuBLAS (17x), and cuRAND (8x).

关键词： ADMM distributed Optimization Message-passing algorithm GPU computing Shared-memory multi-core computing

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New Biology Inspired Anonymous distributed Algorithms to compute Dominating and Total Dominating Sets in Network Graphs 30

New Biology Inspired Anonymous Distributed Algorithms to com...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Luo, Feng Srimani, Pradip K. Clemson Univ Sch Comp Clemson SC 29634 USA

ISBN: (纸本)9781509036820

the selection mechanism of sensory organ precursor (SOP) from pre-neural cells of fruity fly has recently been used to develop a new computing paradigm for probabilistic (randomized) distributed algorithms for graph theoretic primitives like maximal independent set (MIS) in networks [1]. the new paradigm is significantly different from the settings of traditional distributed algorithms [2], [3], [4], [5]: nodes are anonymous, the nodes do not need any knowledge of its neighbors, they receive binary message(s) from the neighbor(s), there is no need to decipher the message, only receipt or non-receipt of message(s) at any step of the algorithm. In this paper, we develop two new simple distributed algorithms to compute the minimal dominated set (MDS) and minimal total dominating set (MTDS) in a graph using this new paradigm. We show that the two algorithms for computing the MDS and MTDS respectively converge with probability (1 - 1/n(2)) using O(D log n) expected number of steps, where n is the number of nodes in the system and D is the maximum degree of a node. Preliminary experimental simulation results show that the algorithms converge in substantially less number of steps to converge than estimated by the analysis for a large number of graph families.

关键词： Biology inspired distributed algorithms Minimal dominating set Minimal total dominating set Synchronous run time scheduler

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Towards a distributed Large-Scale Dynamic Graph Data Store 30

Towards a Distributed Large-Scale Dynamic Graph Data Store

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Iwabuchi, Keita Sallinen, Scott Pearce, Roger Van Essen, Brian Gokhale, Maya Matsuoka, Satoshi Lawrence Livermore Natl Lab Ctr Appl Sci Comp Lawrence KS 94550 USA Tokyo Inst Technol Dept Math & Comp Sci Tokyo Japan Univ British Columbia Dept Elect & Comp Engn Vancouver BC V5Z 1M9 Canada Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan

ISBN: (纸本)9781509036820

In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.

关键词： Bigdata Graph processing

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High Performance parallel Stochastic Gradient Descent in Shared Memory 30

High Performance Parallel Stochastic Gradient Descent in Sha...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Sallinen, Scott Satish, Nadathur Smelyanskiy, Mikhail Sury, Samantika S. Re, Christopher Univ British Columbia Vancouver BC V5Z 1M9 Canada Parallel Comp Lab Santa Clara CA USA Intel Corp Santa Clara CA 95051 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781509021406

Stochastic Gradient Descent (SGD) is a popular optimization method used to train a variety of machine learning models. Most of SGD work to-date has concentrated on improving its statistical efficiency, in terms of rate of convergence to the optimal solution. At the same time, as parallelism of modern CPUs continues to increase through progressively higher core counts, it is imperative to understand the parallel hardware efficiency of SGD, which often comes at odds with its statistical efficiency. In this paper, we explore several modern parallelization methods of SGD on a shared memory system, in the context of sparse and convex optimization problems. Specifically, we develop optimized parallel implementations of several SGD algorithms, and show that their parallel efficiency is severely limited by inter-core communication. We propose a new, scalable, communication-avoiding, many-core friendly implementation of SGD, called HogBatch;which exposes parallelism on several levels, minimizes the impact on statistical efficiency, and, as a result significantly outperforms the other methods. On a variety of datasets, HogBatch demonstrates near linear scalability on a system with 14 cores, as well as delivers up to a 20X speedup over previous methods.

关键词： Program processors

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Exploiting Variant-Based parallelism for Data Mining of Space Weather Phenomena 30

Exploiting Variant-Based Parallelism for Data Mining of Spac...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Gowanlock, Michael Blair, David M. Pankratius, Victor MIT Haystack Observ Cambridge MA 02139 USA

ISBN: (纸本)9781509021406

this paper studies a form of parallelism termed variant-based parallelism, which exploits commonalities and reuse among variant computations in order to improve multithreading scalability. the problem is motivated by space weather studies that aim to identify changes in the Earth's ionosphere caused by auroral activity, tsunamis, and earthquakes. Today it is common to execute cluster algorithm variants with different parameters in order to determine which ones best explain phenomena in empirical data. We propose a novel approach and a set of optimizations to maximize throughput in such clustering algorithms. this is achieved by executing multiple clustering algorithm variants in parallel and developing efficient approaches to concurrently cluster data and maximize the reuse of results from completed variants. We present evaluations on real-world space weather datasets with up to 5 million ionospheric total electron content data points as well as synthetic datasets with up to a million data points. Results show a 1101% performance improvement due to indexing tailored for variant-based clustering, and a 2209% performance improvement when applying all of our proposed optimizations. Our optimizations enable new approaches in computer-aided discovery and could enable the short run times required for early warning systems for natural hazards.

关键词： Computer-Aided Discovery Data Mining DB-SCAN parallel Clustering

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