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A novel parallel algorithm for large-scale Fock matrix construction with small locally distributed memory architectures: RT parallel algorithm.

We developed a novel parallel algorithm for large-scale Fock matrix calculation with small locally distributed memory architectures, and named it the "RT parallel algorithm." The RT parallel algorithm actively involves the concept of integral screening, which is indispensable for reduction of computing times with large-scale biological molecules. The primary characteristic of this algorithm is parallel efficiency, which is achieved by well-balanced reduction of both communicating and computing volume. Only the density matrix data necessary for Fock matrix calculations are communicated, and the data once communicated are reutilized for calculations as many times as possible. The RT parallel algorithm is a scalable method because required memory volume does not depend on the number of basis functions. This algorithm automatically includes a partial summing technique that is indispensable for maintaining computing accuracy, and can also include some conventional methods to reduce calculation times. In our analysis, the RT parallel algorithm had better performance than other methods for massively parallel processors. The RT parallel algorithm is most suitable for massively parallel and distributed Fock matrix calculations for large-scale biological molecules with more than thousands of basis functions.

Algorithms↗

PARODS--a study of parallel algorithms for ordering DNA sequences.

A suite of parallel algorithms for ordering DNA sequences (termed PARODS) is presented. The algorithms in PARODS are based on an earlier serial algorithm, ODS, which is a physical mapping algorithm based on simulated annealing. Parallel algorithms for simulated annealing based on Markov chain decomposition are proposed and applied to the problem of physical mapping. Perturbation methods and problem-specific annealing heuristics are proposed and described. Implementations of parallel Single Instruction Multiple Data (SIMD) algorithms on a 2048 processor MasPar MP-2 system and implementations of parallel Multiple Instruction Multiple Data (MIMD) algorithms on an 8 processor Intel iPSC/860 system are presented. The convergence, speedup and scalability characteristics of the aforementioned algorithms are analyzed and discussed. The best SIMD algorithm is shown to have a speedup of approximately 1000 on the 2048 processor MasPar MP-2 system, whereas the best MIMD algorithm is shown to have a speedup of approximately 5 on the 8 processor Intel iPSC/860 system.

Algorithms↗

A fast parallel algorithm for finding the longest common sequence of multiple biosequences.

BACKGROUND: Searching for the longest common sequence (LCS) of multiple biosequences is one of the most fundamental tasks in bioinformatics. In this paper, we present a parallel algorithm named FAST_LCS to speedup the computation for finding LCS. RESULTS: A fast parallel algorithm for LCS is presented. The algorithm first constructs a novel successor table to obtain all the identical pairs and their levels. It then obtains the LCS by tracing back from the identical character pairs at the last level. Effective pruning techniques are developed to significantly reduce the computational complexity. Experimental results on gene sequences in the tigr database show that our algorithm is optimal and much more efficient than other leading LCS algorithms. CONCLUSION: We have developed one of the fastest parallel LCS algorithms on an MPP parallel computing model. For two sequences X and Y with lengths n and m, respectively, the memory required is max{4*(n+1)+4*(m+1), L}, where L is the number of identical character pairs. The time complexity is O(L) for sequential execution, and O(|LCS(X, Y)|) for parallel execution, where |LCS(X, Y)| is the length of the LCS of X and Y. For n sequences X1, X2, ..., Xn, the time complexity is O(L) for sequential execution, and O(|LCS(X1, X2, ..., Xn)|) for parallel execution. Experimental results support our analysis by showing significant improvement of the proposed method over other leading LCS algorithms.

Algorithms↗

A new parallel algorithm of MP2 energy calculations.

A new parallel algorithm has been developed for second-order Møller-Plesset perturbation theory (MP2) energy calculations. Its main projected applications are for large molecules, for instance, for the calculation of dispersion interaction. Tests on a moderate number of processors (2-16) show that the program has high CPU and parallel efficiency. Timings are presented for two relatively large molecules, taxol (C(47)H(51)NO(14)) and luciferin (C(11)H(8)N(2)O(3)S(2)), the former with the 6-31G* and 6-311G** basis sets (1,032 and 1,484 basis functions, 164 correlated orbitals), and the latter with the aug-cc-pVDZ and aug-cc-pVTZ basis sets (530 and 1,198 basis functions, 46 correlated orbitals). An MP2 energy calculation on C(130)H(10) (1,970 basis functions, 265 correlated orbitals) completed in less than 2 h on 128 processors.

Algorithms↗

Parallel algorithms for arbitrary dimensional Euclidean distance transforms with applications on arrays with reconfigurable optical buses.

In this paper, we present algorithms for computing the Euclidean distance transform (EDT) of a binary image on the array with reconfigurable optical buses (AROB). First, we develop a parallel algorithm termed as Algorithm Expander which can be implemented in O(1) time on an AROB with N x Ndelta processors, where delta = 1/k, k is a constant and a positive integer. Algorithm Expander is designed to compute a higher dimensional EDT based on the computed lower dimensional EDT. It functions as a general EDT expander for us to expand EDT from a lower dimension to a higher dimension. We then develop parallel algorithms for the two-dimensional (2-D)_EDT of a binary image array of size N x N in O(1) time on an AROB with N x N x Ndelta processors and for the three-dimensional (3-D)_EDT of a binary image of size N x N x N in O(1) time on an AROB with N x N x N x Ndelta processors. To the best of our knowledge, all results derived above are the best O(1) time algorithms known. We then extend it to compute the nD_EDT of a binary image of size Nn in O(n) time on an AROB with Nn+delta processors. We also apply our parallel EDT algorithms to build Voronoi diagram and Voronoi polyhetra (polygons), to find all maximal empty spheres and the largest empty sphere, and to compute the medial axis transform. All of these applications can be solved in the same time complexity on an AROB with the same number of processors as needed for solving the EDT problems in the same dimensions.

Journal Article↗

An efficient parallel algorithm for shortest paths in planar layered digraphs.

This paper presents an efficient parallel algorithm for the shortest path problem in planar layered digraphs that runs in O(log3n) time with n processors. The algorithms uses a divide and conquer approach and is based on the novel idea of a one-way separator, which has the property that any directed path can be crossed only once.

Journal Article↗

Optimal parallel algorithm for shortest paths problem on interval graphs.

This paper presents an efficient parallel algorithm for the shortest-path problem in interval graph for computing shortest-paths in a weighted interval graph that runs in O(n) time with n intervals in a graph. A linear processor CRCW algorithm for determining the shortest-paths in an interval graphs is given.

Algorithms↗

Parallel algorithms for the analysis of two-dimensional electrophoresis gels.

This paper describes some parallel processing algorithms for the analysis of two-dimensional electrophoresis images. The machine used for the processing was the CLIP4 Cellular Array Computer at University College, London, one of the largest processor arrays in the world. Included in this paper are an algorithm for centroid detection, Gaussian fitting algorithms, and an algorithm for the extraction of data out of the cellular array machine. It is shown that these parallel algorithms can run at a speed almost completely independent of the number of spots in the gel images.

Algorithms↗

From massively parallel algorithms and fluctuating time horizons to nonequilibrium surface growth

We study the asymptotic scaling properties of a massively parallel algorithm for discrete-event simulations where the discrete events are Poisson arrivals. The evolution of the simulated time horizon is analogous to a nonequilibrium surface. Monte Carlo simulations and a coarse-grained approximation indicate that the macroscopic landscape in the steady state is governed by the Edwards-Wilkinson Hamiltonian. Since the efficiency of the algorithm corresponds to the density of local minima in the associated surface, our results imply that the algorithm is asymptotically scalable.

Journal Article↗

PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis.

The identification of homologous gene families across multiple genomes is a central task in bacterial pangenomics traditionally requiring computationally demanding all-against-all comparisons. PanDelos addresses this challenge with an alignment-free and parameter-free approach based on k-mer profiles, combining high speed, ease of use, and competitive accuracy with state-of-the-art methods. However, the increasing availability of genomic data requires tools that can scale efficiently to larger datasets. To address this need, we present PanDelos-plus, a fully parallel, gene-centric redesign of PanDelos. The algorithm parallelizes the most computationally intensive phases (Best Hit detection and Bidirectional Best Hit extraction) through data decomposition and a thread pool strategy, while employing lightweight data structures to reduce memory usage. Benchmarks on synthetic datasets show that PanDelos-plus achieves up to 14x faster execution and reduces memory usage by up to 96%, while maintaining consistency with the original algorithm. These improvements allow the PanDelos methodology to be applied to population-scale comparative genomics, thus enabling more precise characterisation of pangenome structure and dynamics. PanDelos-plus is available at github.com/synbionics/PanDelos-plus.

Journal Article↗

An efficient parallel algorithm for the calculation of canonical MP2 energies.

We present the parallel version of a previous serial algorithm for the efficient calculation of canonical MP2 energies (Pulay, P.; Saebo, S.; Wolinski, K. Chem Phys Lett 2001, 344, 543). It is based on the Saebo-Almlöf direct-integral transformation, coupled with an efficient prescreening of the AO integrals. The parallel algorithm avoids synchronization delays by spawning a second set of slaves during the bin-sort prior to the second half-transformation. Results are presented for systems with up to 2000 basis functions. MP2 energies for molecules with 400-500 basis functions can be routinely calculated to microhartree accuracy on a small number of processors (6-8) in a matter of minutes with modern PC-based parallel computers.

Journal Article↗

Massively parallel algorithms for chromosome reconstruction.

Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is shown to be isomorphic to the NP-complete Optimal Linear Ordering problem. Massively parallel algorithms for simulated annealing based on Markov chain distribution are proposed and applied to this problem. Perturbation methods and problem-specific annealing heuristics are proposed and described. Experimental results on a 2048 processor MasPar MP-2 system are presented. Convergence, speedup and scalability characteristics of the various algorithms are analyzed and discussed.

Algorithms↗

A parallel algorithm for real-time computation of optical flow.

The precise management of two-dimensional field of velocities from time-varying two-dimensional images is impossible in general. It is, however, possible to compute suitable 'optical flows' that are qualitatively similar to the velocity field in most cases. We describe a simple, parallel algorithm that computes an optical flow from sequences of real images, which is consistent with human psychophysics and suggests plausible physiological models. In particular, our algorithm runs on a Connection Machine supercomputer in close-to-real time. It shows several of the same 'illusions' that are perceived by humans. A natural physiological implementation of the model is consistent with data from cortical areas V1 and MT.

Algorithms↗

Parallel algorithms for graph cycle extraction using the cyclical conjunction operator.

With a view to reducing the computational cost of extracting all the cycles from complex graphs, the authors have examined the viability here of parallel processing. Based on the cyclical conjunction operator, which uses an iterative process to extract every cycle from a graph, a study was performed of the factors intervening in the parallelization of this algorithm, namely the following: granularity of the parallel algorithm, requirements for synchronization points, and the spreading of the load across different processors. Tests were performed on two granularities and four different load distributions. Algorithm implementation is carried out using SGI MP and OpenMP libraries, and, in the light of the present findings, the authors propose a dynamically distributed fine-grain algorithm using that allows all the cycles in a complex graph to be found in an acceptable computational time.

Journal Article↗

Randomized and parallel algorithms for distance matrix calculations in multiple sequence alignment.

Multiple sequence alignment (MSA) is a vital problem in biology. Optimal alignment of multiple sequences becomes impractical even for a modest number of sequences since the general version of the problem is NP-hard. Because of the high time complexity of traditional MSA algorithms, even today's fast computers are not able to solve the problem for large number of sequences. In this paper we present a randomized algorithm to calculate distance matrices, which is a major step in many multiple sequence alignment algorithms. The basic idea employed is sampling (along the lines of). We also illustrate how to parallelize this algorithm. In Section we introduce the problem of multiple sequence alignments. In Section we provide a discussion on various methods that have been employed in the literature for Multiple Sequence Alignment. In this section we also introduce our new sampling approach. We extend our randomized algorithm to the case of non-uniform length sequences as well. We show that our algorithms are amenable to parallelism in Section. In Section we back up our claim of speedup and accuracy with empirical data and examples. In Section we provide some concluding remarks.

Algorithms↗

Parallel algorithm for the computation of the Hartree-Fock exchange matrix: gas phase and periodic parallel ONX.

In this paper we present an efficient parallelization of the ONX algorithm for linear computation of the Hartree-Fock exchange matrix [J. Chem. Phys. 106, 9708 (1997)]. The method used is based on the equal time (ET) partitioning recently introduced [J. Chem. Phys. 118, 9128 (2003)] and [J. Chem. Phys. 121, 6608 (2004)]. ET exploits the slow variation of the density matrix between self-consistent-field iterations to achieve load balance. The method is presented and some benchmark calculations are discussed for gas phase and periodic systems with up to 128 processors. The current parallel ONX code is able to deliver up to 77% overall efficiency for a cluster of 50 water molecules on 128 processors (2.56 processors per heavy atom) and up to 87% for a box of 64 water molecules (two processors per heavy atom) with periodic boundary conditions.

Journal Article↗

Parallel algorithms for querying data on two-dimensional electrophoresis gels.

This paper describes some parallel processing algorithms for the analysis of two-dimensional electrophoresis images. One of the largest processor arrays in the world, CLIP4, at University College London, was used in the research. The paper concentrates on algorithms using CLIP4 for querying the data produced on the gel images. It is shown that many typical queries that researchers request can be answered without removing the data from the processor array, eliminating slow transfer times due to the input/output bottleneck problem. The production of these data by CLIP4 has already been described in two previous papers in this journal. Improvements to the system for the future are also presented.

Algorithms↗

A parallel algorithm for rotating-frame zeugmatography.

An algorithm, which utilizes a high degree of parallel processing, has been developed for two-dimensional rotating-frame zeugmatography so that a picture of 256 X 256 pixels can be generated with a minicomputer system 2 sec after data accumulation. An array processor is employed as a second processor and, the refresh memory of a display unit is used as a fast data storage entity. Approximately half the calculations are carried out in the array processor during the nuclear relaxation period. Data movement to and from the display memory occurs largely during, and in parallel with, the array processing. The sequence can be run at a repetition rate of 35 msec per free induction decay and the algorithm can be implemented in any two-dimensional experiment of the Fourier transform genre.

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