Browsing by Subject "Disks (structural components)"

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Open Access
The method of analytical regularization in the electromagnetic wave scattering by thin disks
(Institution of Engineering and Technology, 2007) Balaban, M. V.; Nosich, A. I.; Altıntaş, Ayhan; Benson, T. M.
We consider the problem of diffraction of an arbitrary electromagnetic wave by a thin disk made from different materials and located in free space. Here we imply a zero- thickness perfectly electrically conducting (PEC) disk, and also thin electrically resistive (ER) and dielectric disks whose thickness is much smaller than the disk radius and the free space wavelength, and also much smaller than the skin-layer depth in the ER disk case. The method used for the modeling is based on the integral equation (IE) technique and analytical regularization. Starting with Maxwell's equations, boundary conditions and the radiation condition at infinity we obtain a set of coupled dual IEs (DIEs) for the unknowns and then reduce this set of equations to the coupled IEs of the Fredholm second kind. To verify our results we calculate the far field characteristics in the case of the PEC disk with the incident field being the field of horizontal electrical dipole located on the disk axis.
Open Access
Profiler and compiler assisted adaptive I/O prefetching for shared storage caches
(ACM, 2008-10) Son, S. W.; Kandemir, M.; Kolcu, I.; Muralidhara, S. P.; Öztürk, Öztürk; Karakoy, M.
I/O prefetching has been employed in the past as one of the mech- anisms to hide large disk latencies. However, I/O prefetching in parallel applications is problematic when multiple CPUs share the same set of disks due to the possibility that prefetches from different CPUs can interact on shared memory caches in the I/O nodes in complex and unpredictable ways. In this paper, we (i) quantify the impact of compiler-directed I/O prefetching - developed originally in the context of sequential execution - on shared caches at I/O nodes. The experimental data collected shows that while I/O prefetching brings benefits, its effectiveness reduces significantly as the number of CPUs is increased; (ii) identify inter-CPU misses due to harmful prefetches as one of the main sources for this re- duction in performance with the increased number of CPUs; and (iii) propose and experimentally evaluate a profiler and compiler assisted adaptive I/O prefetching scheme targeting shared storage caches. The proposed scheme obtains inter-thread data sharing information using profiling and, based on the captured data sharing patterns, divides the threads into clusters and assigns a separate (customized) I/O prefetcher thread for each cluster. In our approach, the compiler generates the I/O prefetching threads automatically. We implemented this new I/O prefetching scheme using a compiler and the PVFS file system running on Linux, and the empirical data collected clearly underline the importance of adapting I/O prefetching based on program phases. Specifically, our pro- posed scheme improves performance, on average, by 19.9%, 11.9% and http://dx.doi.org/10.3% over the cases without I/O prefetching, with independent I/O prefetching (each CPU is performing compiler-directed I/O prefetching independently), and with one CPU prefetching (one CPU is reserved for prefetching on behalf of others), respectively, when 8 CPUs are used. Copyright 2008 ACM.
Open Access
Selective replicated declustering for arbitrary queries
(Springer, 2009-08) Oktay, K. Yasin; Türk, Ata; Aykanat, Cevdet
Data declustering is used to minimize query response times in data intensive applications. In this technique, query retrieval process is parallelized by distributing the data among several disks and it is useful in applications such as geographic information systems that access huge amounts of data. Declustering with replication is an extension of declustering with possible data replicas in the system. Many replicated declustering schemes have been proposed. Most of these schemes generate two or more copies of all data items. However, some applications have very large data sizes and even having two copies of all data items may not be feasible. In such systems selective replication is a necessity. Furthermore, existing replication schemes are not designed to utilize query distribution information if such information is available. In this study we propose a replicated declustering scheme that decides both on the data items to be replicated and the assignment of all data items to disks when there is limited replication capacity. We make use of available query information in order to decide replication and partitioning of the data and try to optimize aggregate parallel response time. We propose and implement a Fiduccia-Mattheyses-like iterative improvement algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to generate a multi-way replicated declustering. Experiments conducted with arbitrary queries on real datasets show that, especially for low replication constraints, the proposed scheme yields better performance results compared to existing replicated declustering schemes. © 2009 Springer.