Fast sorting algorithms on a linear array with a reconfigurable pipelined bus system

We present a fast algorithm for sorting on a linear array with a reconfigurable pipelined bus system (LARPBS), one of the recently proposed parallel architectures based on optical buses. Our algorithm sorts N numbers in O(log N log logN) worst-case time using N processors. To our knowledge, the previous best sorting algorithm on this architecture has a running time of O(log2N).KeywordsLinear ArrayRecursive CallSorting AlgorithmMerging AlgorithmDestination ProcessorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM/sub 1/, MM/sub 2/, MM/sub 3/, and compound algorithms C/sub 1/(/spl epsiv/)and C/sub 2/(/spl delta/). Algorithm C/sub 1/(/spl epsiv/) has adjustable time complexity in sublinear level. Algorithm C/sub 2/(/spl delta/) implies that it is feasible to achieve sublogarithmic time using /spl sigma/(N/sup 3/) processors for matrix multiplication on a realistic system. Algorithms MM/sub 3/, C/sub 1/(/spl epsiv/), and C/sub 2/(/spl delta/) all have o(/spl Nscr//sup 3/) cost and, hence, are very processor efficient. Algorithms MM/sub 1/, MM/sub 3/, and C/sub 1/(/spl epsiv/) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM/sub 2/ and C/sub 2/(/spl delta/) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM/sub 2/ and C/sub 2/(/spl delta/) to unbounded integers and reals are also discussed.

Almost all of the existing two-dimensional block-based medial axis transform (2D_BB_MAT) parallel algorithm are devised for solving the 2D_BB_MAT problem only and are very difficult to extend to a solution of the 3D_BB_MAT problem in parallel. In this paper, we propose efficient O(1) time algorithms for solving the hD_BB_MAT problems, where h is 2 or 3, on the linear array with a reconfigurable pipelined bus system (LARPBS) model. The proposed 2D_BB_MAT algorithm can easily extend to a solution of the 3D_BB_MAT problem in parallel by reducing the hD_BB_MAT problems to the forward chessboard distance transform problems. To the best of our knowledge, the proposed algorithms are the most efficient O(1) time algorithms compared with all the previously published algorithms known.

In this paper, we present an efficient deterministic algorithm for sorting on the LARPBS (Linear Array with a Reconfigurable Pipelined Optical Bus System) model. Our sorting algorithm sorts n general keys in O(logn) time on a LARPBS of size nslogn. The previous best results can sort n items in O(lognloglogn) worst-case time using n processors or sort n items in O((log logn2)worst-case time using n1+ processors. Our algorithm is also scalable to certain extent. When the system size is reduced to nslogn/r for l≤r≤logn, the total computation time is O(rlogn).

Efficient and scalable quicksort on a linear array with a reconfigurable pipelined bus system

In the channel routing problem, terminals between cells are interconnected completely while reducing channel routing area. The crossing number, the number of crossings between two wires in the channel, is related to the channel routing area. In this paper, we present a parallel algorithm finding a maximum crossing number, density, in the channel by using a LARPBS model (Linear Array with a Reconfigurable Pipelined Bus System), which is based on the advanced fiber optic technology. The maximum crossing number algorithm on the LARPBS model runs in O(n) time using O(n) number of processors, where n is the number of nets in a channel.

The optical bus based arrays have recently shown many applications. In this paper we use the pipelining ability of the LARPBS to solve the repetitions detection problem. We propose an algorithm that finds all kind of repetitions in a string including k-power repetitions, overlapping repetitions and non-contiguous repetitions. For a given distance, this operation is performed in O(1) communication cycle with a constant computation time processors on a O(n) processors LARPBS for a string of length n. For all the periods, it is O(n) bus cycle. No previous algorithms were known for this problem on the LARPBS.

In this paper, we present deterministic and probabilistic methods for simulating PRAM computations on linear arrays with reconfigurable pipelined bus systems (LARPBS). The following results are established in this paper. (1) Each step of a p-processor PRAM with meO(p) shared memory cells can be simulated by a p-processors LARPBS in O(log p) time, where the constant in the big-O notation is small. (2) Each step of a p-processor PRAM with meΩ(p) shared memory cells can be simulated by a p-processors LARPBS in O(log m) time. (3) Each step of a p-processor PRAM can be simulated by a p-processor LARPBS in O(log p) time with probability larger than 1−1/p^c for all c>0. (4) As an interesting byproduct, we show that a p-processor LARPBS can sort p items in O(log p) time, with a small constant hidden in the big-O notation. Our results indicate that an LARPBS can simulate a PRAM very efficiently.

We present an O((log log N)/sup 2/) -time algorithm for computing the distance transform of an N /spl times/ N binary image. Our algorithm is designed for the common concurrent read concurrent write parallel random access machine (CRCW PRAM) and requires O(N/sup 2+/spl epsi///log log N) processors, for any /spl epsi/ such that 0 < /spl epsi/ < 1. Our algorithm is based on a novel deterministic sampling scheme and can be used for computing distance transforms for a very general class of distance functions. We also present a scalable version of our algorithm when the number of processors is available p/sup 2+/spl epsi///log log p for some p < N. In this case, our algorithm runs in O((N/sup 2//p/sup 2/)+(N/p) log log p + (log log p)/sup 2/) time. This scalable algorithm is more practical since usually the number of available processors is much less than the size of the image.

The NA48 experiment at the CERN SPS aims to measure the parameter Re(/spl epsiv/'//spl epsiv/) of direct CP violation in the neutral kaon system with an accuracy of 2x10/sup -4/. Based on the requirements of: high event rates (up to 10 kHz) with negligible dead time; support for a variety of detectors with very wide variation in the number of readout channels; data rates of up to 150 MByte/s sustained over the beam burst; level-3 filtering and remote data logging in the CERN computer center; the collaboration has designed and built a modular pipelined data flow system with 40 MHz sampling rate. The architecture combines custom-designed components with commercially available hardware for cost effectiveness and flexibility. To increase the available data bandwidth and to add filtering and monitoring capabilities, the original custom-built event builder hardware has been replaced by a farm of 24 Intel PentiumII based PCs running the Linux operating system during the shutdown between the 1997 and 1998 data taking periods. During the data taking period 1998 the system has been successfully operated taking ca. 70 Terabyte of data.

The Sorting Algorithm proposed by C.A.R Hoare&nbsp; in 1961 by name Quick sort,&nbsp; which is popularly known for being the fastest sorting algorithm. Quick sort&nbsp; is still being practiced in the field of computers systems and its applications. The Algorithm whose efficiency to sort random data set is represented in asymptotic notation as&nbsp; O(n log2 n) and when quick sort algorithm has a input data set which is already Ordered then it takes&nbsp; a quadratic execution time which is considered as a worst case performance and this behavior is represented in asymptotic notation as O(n2). The&nbsp; worst case performance is due the scan over heads which occur over the pre-sorted data set, in other words the partitioning gets skewed due to recursive calls and hence results in a quadratic complexity. This research paper presents an algorithm&nbsp; which minimizes a worst case execution time making it linear when the input list is in non-decreasing order. The paper describes how the improvements are accommodated in the existing quick sort. A priori analysis of&nbsp; proposed algorithm for different cases is made along with a proof of&nbsp; correctness. Later the algorithm is verified for its correctness and asymptotic performance. The algorithm is implemented using C++ and also we have compared with other popular quick-sort version.

All algorithms below are optimal alphabet-independent parallel CRCW PRAM algorithms. In one dimension: Given a pattern string of length m for the string-matching problem, we design an algorithm that computes a deterministic sample of a sufficiently long substring in constant time. This problem used to be a bottleneck in the pattern preprocessing for one- and two-dimensional pattern matching. The best previous time bound was O(log/sup 2/ m/log log m). We use this algorithm to obtain the following results. 1. Improving the preprocessing of the constant-time text search algorithm from O(log/sup 2/ m/log log m) to n(log log m), which is now best possible. 2. A constant-time deterministic string-matching algorithm in the case that the text length n satisfies n=/spl Omega/(m/sup 1+/spl epsiv//) for a constant /spl epsiv/>0. 3. A simple probabilistic string-matching algorithm that has constant time with high probability for random input. 4. A constant expected time Las-Vegas algorithm for computing the period of the pattern and all witnesses and thus string matching itself, solving the main open problem remaining in string matching. >

Order statistics on optically interconnected multiprocessor systems

In this communication, a very simple and extremely fast algorithm is proposed for the pencil beam synthesis of linear sparse arrays having uniform distribution of the excitations. The key idea is that of selecting, as a desired pattern, a Gaussian function having small standard deviation, so as to obtain a narrow beam. This immediately provides the excitation density of the corresponding continuous array of infinite length. Starting from this result and considering a linear array of length $L$ with $N$ elements having equal excitations, an extremely fast and accurate algorithm based on a density tapering approach is proposed that yields suitable positions of the elements, in such a way as to provide an array factor that well approximates the desired pattern. Numerical examples are presented to show the effectiveness of the developed procedure, also when compared with state-of-the-art algorithms. The proposed approach does not consider the mutual coupling between the array elements, but it is numerically shown that this effect produces quite acceptable degradation on the synthesized patterns. Finally, it is shown that also problems involving thousands of elements can be solved in a very accurate way in few milliseconds.

This paper describes a scheme to implement a shared address space of size m on an n-node mesh, with m polynomial in n, where each mesh node hosts a processor and a memory module. At the core of the simulation is a hierarchical memory organization scheme (HMOS), which governs the distribution of the shared variables, each replicated into multiple copies, among the memory modules, through a cascade of bipartite graphs. Based on the expansion properties of such graphs, we devise a protocol that accesses any n-tuple of shared variables in worst-case time $O(n^{1/2+\\eta})$, for any constant $\\eta &gt; 0$, using $O(1/\\eta^{1.59})$ copies per variable, or in worst-case time O(n1/2 log n), using O(log1.59n ) copies per variable. In both cases the access time is close to the natural $O(\\sqrt{n})$ lower bound imposed by the network diameter. A key feature of the scheme is that it can be made fully constructive when m is not too large, thus providing in this case the first efficient, constructive, deterministic scheme in the literature for bounded-degree processor networks. For larger memory sizes, the scheme relies solely on a nonconstructive graph of weak expansion. Finally, the scheme can be efficiently ported to other architectures, as long as they exhibit certain structural properties. In the paper we discuss the porting to multidimensional meshes and to the pruned butterfly, an area-universal network which is a variant of the fat-tree.