The Experts below are selected from a list of 636 Experts worldwide ranked by ideXlab platform
Nasser Peyghambarian - One of the best experts on this subject based on the ideXlab platform.
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phase locked multicore all fiber lasers modeling and experimental investigation
Journal of The Optical Society of America B-optical Physics, 2007Co-Authors: Axel Schülzgen, Valery L Temyanko, Jerome V. Moloney, Nasser PeyghambarianAbstract:A systematic study on a compact, novel phase-locked multicore fiber laser device that is based on a truly all-fiber approach is presented. Supermode selection inside this monolithic fiber device is numerically analyzed, and multicore fiber lasers operating explicitly at the in-phase supermode are experimentally demonstrated.
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phase locking and in phase supermode selection in monolithic multicore fiber lasers
Optics Letters, 2006Co-Authors: Axel Schülzgen, Valery L Temyanko, Jerome V. Moloney, S H Chen, Nasser PeyghambarianAbstract:We report a compact multicore fiber laser that utilizes an all-fiber approach for phase locking and in-phase supermode selection. By splicing passive coreless fibers of controlled lengths to both ends of an active 19-core fiber, we demonstrate that the fundamental in-phase supermode can be selectively excited with a completely monolithic fiber device, instead of conventional free-space and bulk optics, to achieve phase-locked operation for a multiemitter laser device.
Tamer Kahveci - One of the best experts on this subject based on the ideXlab platform.
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A Novel Framework for Large Scale Metabolic Network Alignments by Compression
2012Co-Authors: Michael Dang, Tamer KahveciAbstract:Although the problem of aligning metabolic networks has been considered in the past, the running time and space complexity of these solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can be used with any existing method to significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed Supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing method as our base algorithm. This alignment results in Supernode mappings in the compressed domain, each of which are smaller instances of network alignment. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. Our experiments demonstrate that this method can reduce the sizes of metabolic networks by almost half at each compression level. For the overall framework, we demonstrate how well it increases the performance of an existing alignment method. We observe that we can align twice or more as large networks using the same amount of resources with our framework compared to a recent method for network alignment, namely SubMAP. Our results also suggest that the alignment obtained by only one level of compression captures the original alignment results with very high accuracy. 1
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Metabolic network alignment in large scale by network compression
BMC Bioinformatics, 2012Co-Authors: Michael Dang, Tamer KahveciAbstract:Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase , the alignment phase and the refinement phase . For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed Supernodes , and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in Supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our framework results in alignments that are comparable to existing algorithms and can do this with practical resource utilization for large scale networks that existing algorithms could not handle. As an example of our method's performance in practice, the alignment of organism-wide metabolic networks of human (1615 reactions) and mouse (1600 reactions) was performed under three minutes by only using a single level of compression.
Klaus Gartner - One of the best experts on this subject based on the ideXlab platform.
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solving unsymmetric sparse systems of linear equations with pardiso
Future Generation Computer Systems, 2004Co-Authors: Olaf Schenk, Klaus GartnerAbstract:Supernode partitioning for unsymmetric matrices together with complete block diagonal Supernode pivoting and asynchronous computation can achieve high gigaflop rates for parallel sparse LU factorization on shared memory parallel computers. The progress in weighted graph matching algorithms helps to extend these concepts further and unsymmetric prepermutation of rows is used to place large matrix entries on the diagonal. Complete block diagonal Supernode pivoting allows dynamical interchanges of columns and rows during the factorization process. The level-3 BLAS efficiency is retained and an advanced two-level left-right looking scheduling scheme results in good speedup on SMP machines. These algorithms have been integrated into the recent unsymmetric version of the PARDISO solver. Experiments demonstrate that a wide set of unsymmetric linear systems can be solved and high performance is consistently achieved for large sparse unsymmetric matrices from real world applications.
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solving unsymmetric sparse systems of linear equations with pardiso
International Conference on Computational Science, 2002Co-Authors: Olaf Schenk, Klaus GartnerAbstract:Supernode pivoting for unsymmetric matrices coupled with Supernode partitioning and asynchronous computation can achieve high gigaflop rates for parallel sparse LU factorization on shared memory parallel computers. The progress in weighted graph matching algorithms helps to extend these concepts further and prepermutation of rows is used to place large matrix entries on the diagonal. Supernode pivoting allows dynamical interchanges of columns and rows during the factorization process. The BLAS-3 level efficiency is retained. An enhanced left-right looking scheduling scheme is uneffected and results in good speedup on SMP machines without increasing the operation count. These algorithms have been integrated into the recent unsymmetric version of the PARDISO solver. Experiments demonstrate that a wide set of unsymmetric linear systems can be solved and high performance is consistently achieved for large sparse unsymmetric matrices from real world applications.
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Scalable Parallel Sparse LU Factorization with a Dynamical Supernode Pivoting Approach in Semiconductor Device Simulation
2000Co-Authors: Klaus Gartner, Olaf Schenk, Wolfgang FichtnerAbstract:A novel parallel LU factorization algorithm for general sparse linear systems arising in semiconductor device simulation problems is presented. In order to improve sequential and parallel sparse numerical factorization performance, the proposed methods are based on a Level-3 BLAS update and pipelining parallelism is exploited with a combination of left- and right- looking Supernode techniques. The parallel pivoting methods allow complete or Bunch and Kaufman Supernode pivoting in order to compromise numerical stability and scalability during the factorization process. For sufficiently large problem sizes numerical experiments demonstrate that the scalability of the parallel algorithm is nearly independent of the special SMP architecture (speedup of up to seven using eight processors). The approach is based on OpenMP directives and has been successfully tested on the following parallel computers: COMPAQ AlphaServer, SGI Origin 2000, SUN Enterprise Server, Intel Pentium III/Win..
Stefano Longhi - One of the best experts on this subject based on the ideXlab platform.
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non hermitian gauged topological laser arrays
Annalen der Physik, 2018Co-Authors: Stefano LonghiAbstract:Stable and phase-locked emission in an extended topological supermode of coupled laser arrays, based on concepts of non-Hermitian and topological photonics, is theoretically suggested. We consider a non-Hermitian Su-Schrieffer-Heeger chain of coupled microring resonators and show that application of a synthetic imaginary gauge field via auxiliary passive microrings leads to all supermodes of the chain, except one, to become edge states. The only extended supermode, that retains some topological protection, can stably oscillate suppressing all other non-topological edge supermodes. Numerical simulations based on a rate equation model of semiconductor laser arrays confirm stable anti-phase laser emission in the extended topological supermode and the role of the synthetic gauge field to enhance laser stability.
Adnan Yazici - One of the best experts on this subject based on the ideXlab platform.
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an adaptive energy aware and distributed fault tolerant topology control algorithm for heterogeneous wireless sensor networks
Ad Hoc Networks, 2016Co-Authors: Fatih Deniz, Hakki Bagci, Ibrahim Korpeoglu, Adnan YaziciAbstract:This paper introduces an adaptive, energy-aware and distributed fault-tolerant topology-control algorithm, namely the Adaptive Disjoint Path Vector (ADPV) algorithm, for heterogeneous wireless sensor networks. In this heterogeneous model, we have resource-rich Supernodes as well as ordinary sensor nodes that are supposed to be connected to the Supernodes. Unlike the static alternative Disjoint Path Vector (DPV) algorithm, the focus of ADPV is to secure Supernode connectivity in the presence of node failures, and ADPV achieves this goal by dynamically adjusting the sensor nodes' transmission powers. The ADPV algorithm involves two phases: a single initialization phase, which occurs at the beginning, and restoration phases, which are invoked each time the network's Supernode connectivity is broken. Restoration phases utilize alternative routes that are computed at the initialization phase by the help of a novel optimization based on the well-known set-packing problem. Through extensive simulations, we demonstrate that ADPV is superior in preserving Supernode connectivity. In particular, ADPV achieves this goal up to a failure of 95% of the sensor nodes; while the performance of DPV is limited to 5%. In turn, by our adaptive algorithm, we obtain a two-fold increase in Supernode-connected lifetimes compared to DPV algorithm.
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a distributed fault tolerant topology control algorithm for heterogeneous wireless sensor networks
IEEE Transactions on Parallel and Distributed Systems, 2015Co-Authors: Hakki Bagci, Ibrahim Korpeoglu, Adnan YaziciAbstract:This paper introduces a distributed fault-tolerant topology control algorithm, called the Disjoint Path Vector (DPV), for heterogeneous wireless sensor networks composed of a large number of sensor nodes with limited energy and computing capability and several Supernodes with unlimited energy resources. The DPV algorithm addresses the $k$ -degree Anycast Topology Control problem where the main objective is to assign each sensor’s transmission range such that each has at least $k$ -vertex-disjoint paths to Supernodes and the total power consumption is minimum. The resulting topologies are tolerant to $k-1$ node failures in the worst case. We prove the correctness of our approach by showing that topologies generated by DPV are guaranteed to satisfy $k$ -vertex Supernode connectivity. Our simulations show that the DPV algorithm achieves up to 4-fold reduction in total transmission power required in the network and 2-fold reduction in maximum transmission power required in a node compared to existing solutions.
