C3: A Parallel Model for Coarse-Grained Machines

Hambrusch, Susanne E.; Khokhar, Ashfaq

doi:10.1006/jpdc.1996.0010

Cited by 23 publications

(11 citation statements)

References 24 publications

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“…For the computation and communication analysis we use a coarse grained computing model such as C 3 -model [48] and [49]. Also, for analysis purposes, we assume that the Clustalw System [33] is being used at each processor as the underlying profile-profile alignment system.…”

Section: Analysis Of Computation and Communication Costsmentioning

confidence: 99%

A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes

Saeed

Perez-Rathke

Gwarnicki

et al. 2012

Journal of Parallel and Distributed Computing

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Genome resequencing with short reads generated from pyrosequencing generally relies on mapping the short reads against a single reference genome. However, mapping of reads from multiple reference genomes is not possible using a pairwise mapping algorithm. In order to align the reads w.r.t each other and the reference genomes, existing multiple sequence alignment(MSA) methods cannot be used because they do not take into account the position of these short reads with respect to the genome, and are highly inefficient for large number of sequences. In this paper, we develop a highly scalable parallel algorithm based on domain decomposition, referred to as P-Pyro-Align, to align such large number of reads from single or multiple reference genomes. The proposed alignment algorithm accurately aligns the erroneous reads, and has been implemented on a cluster of workstations using MPI library. Experimental results for different problem sizes are analyzed in terms of execution time, quality of the alignments, and the ability of the algorithm to handle reads from multiple haplotypes. We report high quality multiple alignment of up to 0.5 million reads. The algorithm is shown to be highly scalable and exhibits super-linear speedups with increasing number of processors.

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Section: Analysis Of Computation and Communication Costsmentioning

confidence: 99%

A high performance multiple sequence alignment system for pyrosequencing reads from multiple reference genomes

Saeed

Perez-Rathke

Gwarnicki

et al. 2012

Journal of Parallel and Distributed Computing

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“…Our indirect algorithms perform R x (K , P) in at most log K + 2 steps. 3 The algorithm is applicable for any value of K , including prime numbers. For example, when K = 31 and P = 64, the redistribution can be performed in six communication steps.…”

Section: Cyclic(x)mentioning

confidence: 99%

“…We now consider the cyclic(x) to cyclic(K x) redistribution problem, R x (K , P). Since this redistribution problem becomes the all-to-all communication problem when K ≥ P, we consider the case of K < P. Figure 1 shows the example of R 2 (3,4). The array A which has N = 48 elements is shown in Figure 1(a).…”

Section: Cyclic(x)mentioning

confidence: 99%

“…The communication overheads can be represented using an analytical model of typical distributed memory machines, the General purpose Distributed Memory (GDM) model [24]. Similar models are reported in the literature [1], [3], [4]. The GDM model represents the communication time of a message passing operation using two parameters: the start-up time T d and the unit data transmission time τ d .…”

Section: The Cost Of Redistributionmentioning

confidence: 99%

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Efficient Algorithms for Block-Cyclic Redistribution of Arrays

1999

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The block-cyclic data distribution is commonly used to organize array elements over the processors of a coarse-grained distributed memory parallel computer. In many scientific applications, the data layout must be reorganized at run-time in order to enhance locality and reduce remote memory access overheads. In this paper we present a general framework for developing array redistribution algorithms. Using this framework, we have developed efficient algorithms that redistribute an array from one block-cyclic layout to another.Block-cyclic redistribution consists of index set computation, wherein the destination locations for individual data blocks are calculated, and data communication, wherein these blocks are exchanged between processors. The framework treats both these operations in a uniform and integrated way. We have developed efficient and distributed algorithms for index set computation that do not require any interprocessor communication. To perform data communication in a conflict-free manner, we have developed direct, indirect, and hybrid algorithms. In the direct algorithm, a data block is transferred directly to its destination processor. In an indirect algorithm, data blocks are moved from source to destination processors through intermediate relay processors. The hybrid algorithm is a combination of the direct and indirect algorithms.Our framework is based on a generalized circulant matrix formalism of the redistribution problem and a general purpose distributed memory model of the parallel machine. Our algorithms sustain excellent performance over a wide range of problem and machine parameters. We have implemented our algorithms using MPI, to allow for easy portability across different HPC platforms. Experimental results on the IBM SP-2 and the Cray T3D show superior performance over previous approaches. When the block size of the cyclic data layout changes by a factor of K , the redistribution can be performed in O(log K ) communication steps. This is true even when K is a prime number. In contrast, previous approaches take O(K ) communication steps for redistribution.Our framework can be used for developing scalable redistribution libraries, for efficiently implementing parallelizing compiler directives, and for developing parallel algorithms for various applications. Redistribution algorithms are especially useful in signal processing applications, where the data access patterns change significantly between computational phases. They are also necessary in linear algebra programs, to perform matrix transpose operations.

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“…Computational geometry algorithms for the CGM have been presented in [BxHr01,BMR98,BMR99,DFR93,FRU95,DDDFK95]; also see [Dehn99]. Other computational models for coarse grained parallel computers include [Vali90,CKPSSSSE,HaK93].…”

Section: Introductionmentioning

confidence: 99%