Model Of Parallel Computation Research Articles

On String Languages Generated by Spiking Neural P Systems With Structural Plasticity.

Spiking neural P systems (SNP systems) are parallel and non-deterministic models of computation, inspired by the neural system of the brain. A variant of SNP systems known as SNP systems with structural plasticity (SNPSP systems) includes the feature of adding or removing synapses among neurons. This feature is inspired by plasticity from neuroscience during cognition and learning. Despite the reductionist framework of SNP and SNPSP systems, such as brain-like systems are capable of computational universality. In particular, we use SNPSP systems in this paper to compute some classes of languages from the Chomsky hierarchy: FIN, REG, and RE. The computations of such classes continue a research direction established in the previous paper. We also emphasize the (dis)advantages of synapse plasticity in the neural system, compared with existing features of SNP systems, when generating languages.

Scalability Evaluation of Cimmino Algorithm for Solving Linear Inequality Systems on Multiprocessors with Distributed Memory

The paper is devoted to a scalability study of Cimmino algorithm for linear inequality systems. This algorithm belongs to the class of iterative projection algorithms. For the analytical analysis of the scalability, the BSF (Bulk Synchronous Farm) parallel computation model is used. An implementation of the Cimmino algorithm in the form of operations on lists using higher-order functions Map and Reduce is presented. An analytical estimation of the upper scalability bound of the algorithm for cluster computing systems is derived. An information about the implementation of Cimmino algorithm on lists in C++ language using the BSF program skeleton and MPI parallel programming library is given. The results of large-scale computational experiments performed on a cluster computing system are demonstrated. A conclusion about the adequacy of the analytical estimations by comparing them with the results of computational experiments is made.

On Distributed Solution to SAT by Membrane Computing

Tissue P systems with evolutional communication rules and cell division (TPec, for short) are a class of bio-inspired parallel computational models, which can solve NP-complete problems in a feasible time. In this work, a variant of TPec, called $k$-distributed tissue P systems with evolutional communication and cell division ($k\text{-}\Delta_{TP_{ec}}$, for short) is proposed. A uniform solution to the SAT problem by $k\text{-}\Delta_{TP_{ec}}$ under balanced fixed-partition is presented. The solution provides not only the precise satisfying truth assignments for all Boolean formulas, but also a precise amount of possible such satisfying truth assignments. It is shown that the communication resource for one-way and two-way uniform $k$-P protocols are increased with respect to $k$; while a single communication is shown to be possible for bi-directional uniform $k$-P protocols for any $k$. We further show that if the number of clauses is at least equal to the square of the number of variables of the given boolean formula, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem are more efficient than TPec as show in \cite{bosheng2017}; if the number of clauses is equal to the number of variables, then $k\text{-}\Delta_{TP_{ec}}$ for solving the SAT problem work no much faster than TPec.

A novel parallel distance metric-based approach for diversified ranking on large graphs

Diversified ranking on graphs (DRG) is an important and challenging issue in researching graph data mining. Traditionally, this problem is modeled by a submodular optimization objective, and solved by applying a cardinality constrained monotone submodular maximization. However, the existing submodular objectives do not directly capture the dis-similarity over pairs of nodes, while most of algorithms cannot easily take full advantage of the power of a distributed cluster computing platform, such as Spark, to significantly promote the efficiency of algorithms. To overcome the deficiencies of existing approaches, in this paper, a generalized distance metric based on a subadditive set function over the symmetry difference of neighbors of pairs of nodes is introduced to capture the pairwise dis-similarity over pairs of nodes. In our approach,DRG is formulated as a Max-Sum k-dispersion problem with metrical edge weights, which is NP-hard, in association with the proposed distance metric, a centralized linear time 2-approximation algorithm GA is then developed to significantly solve the problem ofDRG. Moreover, we develop a highly parallelizable algorithm forDRG, which can be easily implemented in MapReduce style parallel computation models using GA as a basic reducer. Finally, extensive experiments are conducted on real network datasets to verify the effectiveness and efficiency of our proposed approaches.

Модель параллельных вычислений для многопроцессорных систем с распределенной памятью

The paper is devoted to an analytical study of the "master-worker" framework scalability on multiprocessors with distributed memory. A new model of parallel computations called BSF is proposed. The BSF model is based on BSP and SPMD models. The scope of BSF model is the compute-intensive applications. The architecture of BSF-computer is defined. The structure of BSF-program is described. The Using this metric, the upper scalability bounds of BSF programs on distributed memory multiprocessors are evaluated. The formulas for estimating the parallel efficiency of BSF programs also proposed.

Analytical Estimation of the Scalability of Iterative Numerical Algorithms on Distributed Memory Multiprocessors

This article presents a new high-level parallel computational model named BSF - Bulk Synchronous Farm. The BSF model extends the BSP model to deal with the compute-intensive iterative numerical methods executed on distributed-memory multiprocessor systems. The BSF model is based on the master-worker paradigm and the SPMD programming model. The BSF model makes it possible to predict the upper scalability bound of a BSF-program with great accuracy. The BSF model also provides equations for estimating the speedup and parallel efficiency of a BSF-program.

Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

Leveraging community-contributed information such as blogs, Global Positioning System, and images that are geographically tagged for suggestions on navigation with mobile based communication is becoming a research area. The travel recommender a system is based on Points Of Interests (POIs) of user’s emerge in many studies and enterprise applications. Most of the preceeding travel recommendation methodologies are capable of recommending a personalized travel by considering the POIs. The major objective of this paper is to design a new Multi-Ontology based Points of Interest (MO-POIs) travel recommendation system which considers the POIs of the user along with the individual semantic information. Initially stage, the personalized MO-POIs sequence is recommended, and the routes are ranked based on the individual depending on the package he uses. Both the user and route package similarities are evaluated using parallel fuzzy clustering algorithm in this research work. Map Reduce is used as a parallel computation model in this research which performs computation with the help of a couple of functions that are termed as map and reduce. The social users travel records are used to optimize the top ranked routes. Finally, the new MO-POIs recommendation system evaluation is finished with a billion of Flicker images, uploaded by thousands of users and also more than 24,000 travelogues that cover the estimated 850 and above travel POIs in nine renowned cities, and illustrates its efficiency. The results are measured using the performance metrics like average precision and weighted average precision. The evaluation concludes that the proposed algorithm is capable of performing well in recent works that uses travel logs.

A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots’ CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization; 2) adaptive logistic regression-based correlation analysis; and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis.

Editorial for the Special Issue on Parallel and Concurrent Functional Programming

Functional languages are uniquely suited to providing programmers with a programming model for parallel and concurrent computing. This is reflected in the wide range of work that is currently underway, both on parallel and concurrent functional languages, as well as on bringing functional language features to other programming languages. This has resulted in a rapidly growing number of practical applications. The Journal of Functional Programming decided to dedicate a special issue to this field to showcase the state of the art in how functional languages and functional concepts currently assist programmers with the task of managing the challenges of creating parallel and concurrent systems.

Development of DEPSO Island Model with Particle Speed Limit for Distribution Network Reconfigurations

Abstract This paper presents a new metaheuristic method for distribution network reconfigurations. In recent years, distribution network reconfigurations play an important role to reduce the network loss with sectionalizing switches and shunt capacitors. Distribution network reconfigurations may be formulated as a combinatorial optimization of the NP hard problem. In this paper, Parallel Discrete Evolutionary Particle Swarm Optimization (PDEPSO) is proposed to deal with distribution network reconfigurations. PDEPSO is Discrete Particle Swarm Optimization (DEPSO) with the island model of parallel computation in which DEPSO integrates EPSO of adaptive PSO with the sigmoid function to handle combinatorial optimization problems. Furthermore, to improve the performance of PDEPSO, this paper develops a method that sets the particle speed limit of DEPSO to maintain the solution diversity and to evaluate better solutions. The effectiveness of the proposed method is demonstrated in the IEEE 69-node distribution system.

Performance and Power Efficient Massive Parallel Computational Model for HPC Heterogeneous Exascale Systems

The emerging high-performance computing Exascale supercomputing system, which is anticipated to be available in 2020, will unravel many scientific mysteries. This extraordinary processing framework will accomplish a thousand-folds increment in figuring power contrasted with the current Petascale framework. The prospective framework will help development communities and researchers in exploring from conventional homogeneous to the heterogeneous frameworks that will be joined into energy efficient GPU devices along with traditional CPUs. For accomplishing ExaFlops execution through the Ultrascale framework, the present innovations are confronting several challenges. Huge parallelism is one of these challenges, which requires a novel low power consuming parallel programming approach for attaining massive performance. This paper introduced a new parallel programming model that achieves massive parallelism by combining coarse-grained and fine-grained parallelism over inter-node and intra-node computation respectively. The proposed framework is tri-hybrid of MPI, OpenMP, and compute unified device architecture (MOC) that compute input data over heterogeneous framework. We implemented the proposed model in linear algebraic dense matrix multiplication application, and compared the quantified metrics with well-known basic linear algebra subroutine libraries such as CUDA basic linear algebra subroutines library and KAUST basic linear algebra subprograms. MOC outperformed to all implemented methods and achieved massive performance by consuming less power. The proposed MOC approach can be considered an initial and leading model to deal emerging Exascale computing systems.

Heterogeneous parallel computing accelerated iterative subpixel digital image correlation

Parallel computing techniques have been introduced into digital image correlation (DIC) in recent years and leads to a surge in computation speed. The graphics processing unit (GPU)-based parallel computing demonstrated a surprising effect on accelerating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI) is estimated through the fast Fourier transform-based cross-correlation (FFT-CC) algorithm. Based on the performance evaluation, a heterogeneous parallel computing (HPC) model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame.

Solving the 0-1 knapsack problem based on a parallel intelligent molecular computing model system

Solving the 0-1 knapsack problem based on a parallel intelligent molecular computing model system

Parallel Tree Search in Volunteer Computing: a Case Study

While volunteer computing, as a restricted model of parallel computing, has proved itself to be a successful paradigm of scientific computing with excellent benefit on cost efficiency and public outreach, many problems it solves are intrinsically highly parallel. However, many efficient algorithms, including backtracking search, take the form of a tree search on an extremely uneven tree that cannot be easily parallelized efficiently in the volunteer computing paradigm. We explore in this article how to perform such searches efficiently on volunteer computing projects. We propose a parallel tree search scheme, and we describe two examples of its real-world implementation, Harmonious Tree and Odd Weird Search, both carried out at the volunteer computing project yoyo@home. To confirm the observed efficiency of our scheme, we perform a mathematical analysis, which proves that, under reasonable assumption that agrees with experimental observation, our scheme is only a constant multiplicative factor away from perfect parallelism. Details on improving the overall performance are also discussed.

Cellular matrix model for parallel combinatorial optimization algorithms in Euclidean plane

We propose a parallel computation model, called cellular matrix model (CMM), to address large-size Euclidean graph matching problems in the plane. The parallel computation takes place by partitioning the plane into a regular grid of cells, each cell being affected to a single processor. Each processor operates on local data, starting from its cell location and extending its search to the neighborhood cells in a spiral search way. In order to deal with large-size problems, memory size and processor number are fixed as O(N), where N is the problem size. Then one key point is that closest point searching in the plane is performed in O(1) expected time for uniform or bounded distribution, for each processor independently. We define a generic loop that models the parallel projection between graphs and their matching, as executed by the many cells at a given level of computation granularity. To illustrate its efficacy and versatility, we apply the CMM, on GPU platforms, to two problems in image processing: superpixel segmentation and stereo matching energy minimization. Firstly, we propose an extended version of the well-known SLIC superpixel segmentation algorithm, which we call SPASM algorithm, by using a parallel 2D self-organizing map instead of k-means algorithm. Secondly, we investigate the idea of distributed variable neighborhood search, and propose a parallel search heuristic, called distributed local search (DLS), for global energy minimization of stereo matching problem. We evaluate the approach with regards to the state-of-the-art graph cut and belief propagation algorithms. For each problem, we argue that the parallel GPU implementation provides new competitive quality/time trade-offs, with substantial acceleration factors as the problem size increases.

Spiking Neural P Systems With Polarizations.

Spiking neural P (SN P) systems are a class of parallel computation models inspired by neurons, where the firing condition of a neuron is described by a regular expression associated with spiking rules. However, it is NP-complete to decide whether the number of spikes is in the length set of the language associated with the regular expression. In this paper, in order to avoid using regular expressions, two major and rather natural modifications in their form and functioning are proposed: the spiking rules no longer check the number of spikes in a neuron, but, in exchange, a polarization is associated with neurons and rules, one of the three electrical charges -, 0,+. Surprisingly enough, the computing devices obtained are still computationally complete, which are able to compute all Turing computable sets of natural numbers. On this basis, the number of neurons in a universal SN P system with polarizations is estimated. Several research directions are mentioned at the end of this paper.

Fuzzy alternating Büchi automata over distributive lattices

Alternation was first proposed by Chandra et al. for obtaining a theoretical model of parallel computations. Compared with nondeterminism, alternation gives computing devices the power of universal choice in addition to existential choice. In this paper, we put forward a notion of fuzzy alternating Büchi automata over a distributive lattice L (L-ABAs, for short). In our setting, a weight is the label of a leaf node of the run tree when executing a transition, which is helpful in complementing L-ABAs. Taking the dual operation on the transition function and negating final costs on states, we can get the complement of a given L-ABA. We point out that L-ABAs have the same expressive power as L-valued fuzzy nondeterministic Büchi automata (L-NBAs). A construction presented here shows that languages accepted by L-valued fuzzy alternating co-Büchi automata (L-ACAs) are also ω-regular languages. Moreover, closure properties of L-ABAs and decision problems for L-ABAs are also discussed.

Cell-Like Spiking Neural P Systems With Request Rules.

Cell-like spiking neural (cSN) P systems are a class of distributed and parallel computation models inspired by both the way in which neurons process information and communicate to each other by means of spikes and the compartmentalized structures of living cells. cSN P systems have been proved to be Turing universal if more spikes can be produced by consuming some spikes or spikes can be replicated. In this paper, in order to answer the open problem whether this functioning of producing more spikes and replicating spikes can be avoided by using some strategy without the loss of computation power, we introduce cSN P systems with request rules, which have classical spiking rules and forgetting rules, and also request rules in the skin membrane. The skin membrane can receive spikes from the environment by the application of request rules. cSN P systems with request rules are proved to be Turing universal. The results show that the decrease of computation power caused by removing the internal functioning of producing more spikes and replicating spikes can be compensated by request rules, which suggests that the communication between a cell and the environment is an essential ingredient of systems in terms of computation power.

Solving combinatorial problems using a parallel framework

This paper presents a new IBobpp framework which is an improvement of a high level parallel programming framework called Bobpp to optimize the performance of solving combinatorial problems. The Bobpp parallel computation model is as the majority of parallel models proposed in the context of tree search algorithms with node oriented parallelization, meaning that at every step of the algorithm, each thread gets one node from a unique global pool of non-explored nodes, generates the child nodes, and reinserts them into the pool to be explored later. This classical model has two drawbacks. First, the use of many threads creates a bottleneck problem. Second, grabbing a node causes memory contention problem when many nodes are generated and inserted into the same pool. To solve these problems, IBobpp framework proposes three solutions. The first consists of using multiple pools of nodes shared between all threads. The second solution consists of using a new computation model. The third solution consists of hybridization of the two previous solutions. Preliminary result shows that IBobpp gives a good result using the third solution.

Research on the Design of Parallel Hypernetworks Model Based on Hadoop Cloud Computing Platform

The evolutionary hypernetwork is a kind of cognitive learning model inspired by biological networks and the hypernetwork model has been successfully applied in plenty of fields. But the hypernetwork model may lead to a huge amount of hyperedges in the process of high dimensional data, which is the bottleneck of the single machine hardware implementation. In order to solve this problem, this paper presents the parallel model of hypernetwork computation under the Hadoop cloud computing platform, which changes single machine algorithm to multiple machines. Empirical studies on the acute leukemia dataset result demonstrate that the proposed hypernetwork classifier of parallel evolutionary hypernetwork model based on the Hadoop cloud computing platform has higher classification accuracy rate and good scalability. This is the main advantage of the parallel evolutionary hypernetwork model.