Model Of Parallel Computation Research Articles

Spiking neural P systems with target indications

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information by means of spikes, where a neuron fires and distributes the spikes to all the neurons linked by synapses with the firing neuron. In this work, a new spike distribution mechanism is introduced, where the produced spikes are distributed to a set of neurons indicated by the target indications. The computation power of SNP systems with this new spike distribution mechanism is investigated. It is demonstrated that SNP systems with such a distribution mechanism are Turing universal as both number generators and function computing devices. Moreover, it is shown that 6 neurons (respectively, 15 neurons) are sufficient for constructing a universal SNP system with the proposed spike distribution mechanism as a number generator (respectively, as a function computing device). By comparing with the classical one, it can be found that the proposed spike distribution mechanism is a powerful feature in terms of the number of neurons used to construct universal SNP systems.

Evolution of Visualization Methods for Research Publication Collections

It is proposed to add a static system of types to the dataflow functional model of parallel computing and the dataflow functional parallel programming language developed on its basis. The use of static typing increases the possibility of transforming dataflow functional parallel programs into programs running on modern parallel computing systems. Language constructions are proposed. Their syntax and semantics are described. It is noted that the need to use the single assignment principle in the formation of data storages of a particular type. The features of instrumental support of the proposed approach are considered.

Parallel algorithms for finding connected components using linear algebra

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.

The computation power of spiking neural P systems with polarizations adopting sequential mode induced by minimum spike number

Spiking neural P systems (SN P systems) refer to a type of distributed parallel neural computation model in the membrane computing framework. In this work, a new type of SN P system is examined, i.e., spiking neural P systems with polarizations (PSN P systems), by taking inspiration from the polarized cell membrane of a neuron. For the PSN P system, the firing condition of rules is the neuron-associated polarization. This work focuses on examining the computation power of the PSN P systems adopting the sequential mode induced by the minimum spike number, where at a computation step, one (resp., all) of neurons that hold the minimum spike number within the neurons which can fire will fire, i.e., the min-sequentiality strategy (resp., min-pseudo-sequentiality strategy). We prove that PSN P systems adopting the min-sequentiality strategy or min-pseudo-sequentiality strategy are Turing universal as the number generating devices. These results indicate the computation power of PSN P systems is robust regarding their strategies of sequentiality.

Improving Processing Speed of Real Time Stereo Matching u sing Heterogenous CPU GPU Model

This paper presents an improvement of the processing speed of the stereo matching problem. The time required for stereo matching represents a problem for many real time applications such as robot navigation , self-driving vehicles and object tracking. In this work, a real-time stereo matching system is proposed that utilizes the parallelism of Graphics Processing Unit (GPU). An area based stereo matching system is used to generate the disparity map. Four different sequential and parallel computational models are used to analyze the time consumed by the stereo matching. The models are: 1) Sequential CPU, 2) Parallel multi-core CPU, 3) Parallel GPU and 4) Parallel heterogenous CPU/GPU. The dense disparity image is calculated, and the time is highly reduced using the heterogenous CPU/GPU model, while maintaining the same accuracy of other models. A static partitioning of CPU and GPU workload is properly designed based on time analysis. Different cost functions are used to measure the correspondence and to generate the disparity map. A sliding window is used to calculate the cost functions efficiently. A speed of more than 100 frames per second(f/s) is achieved using parallel heterogenous CPU/GPU for 640 x 480 image resolution and a disparity range equals 50.

Topological dynamics of Nondeterministic Cellular Automata

Cellular Automata (CA) are discrete dynamical systems and an abstract model of parallel computation. Nondeterministic Cellular Automata (NCA) are the class of multi-valued functions obtained by allowing nondeterminism in CA. In this study we extend to multi-valued functions the definition of some important topological properties and investigate the differences between the dynamical behaviour of one-dimensional NCA and one-dimensional CA in such classes.

Cluster-Based and GPU-Driven Parallel Computing Model to Accelerate Circuit Simulation

In this digital age circuit design, analysis and validation is not only fundamental step but quite crucial in all the industries and in research. Simulation software is available for circuit analysis, but they all prove to be slower for very large circuit simulation or to execute thousands of iteration of transient analysis. Accelerating simulator is as important as speeding up circuit design. In this paper we have addressed circuit analysis using parallel computing approach on Graphics Processing Unit (GPU). Now a day’s high end GPUs are available with sufficient memory in the architecture itself. Circuit processing functions are analysed to search compute intensive functions. Mathematical operations are redesigned so that it will execute in parallel. LU decomposition algorithm and complex math operations are converted in parallel form. Some mathematical operations are simplified to merge them in suitable cluster. Clustering approach is used which helps in finding kernel of uniform operations to map on GPU cores. GPU programming strategies like if-else in-lining, parallel reduction etc are useful in accelerating circuit operations. Use of look up tables in shared memory or constant memory proves to be useful in fast data access. At least 15% speed gain is achieved for operational analysis and 40% for transient analysis of regular circuits.

On the performance difference between theory and practice for parallel algorithms

The performance of parallel algorithms is often inconsistent with their preliminary theoretical analyses. Indeed, the difference is increasing between the ability to theoretically predict the performance of a parallel algorithm and the results measured in practice. This is mainly due to the accelerated development of advanced parallel architectures, whereas there is still no agreed model for parallel computation, which has implications for the design of parallel algorithms and for the manner in which parallel programming should be taught.In this study, we examined the practical performance of Cormen’s Quicksort parallel algorithm. We determined the performance of the algorithm with different parallel programming approaches and examine the capacity of theoretical performance analyses of the algorithm for predicting the actual performance. This algorithm is used for teaching theoretical and practical aspects of parallel programming to undergraduate students. We considered the pedagogic implications that may arise when the algorithm is used as a learning resource for teaching parallel programming.

Processing streams in a monitoring cloud cluster

The creation of monitoring clusters based on cloud computing technologies is a promising direction for the development of systems for continuous monitoring of objects for various purposes in the web space. Hadoop web-programming environment is the technological basis for the development of algorithmic and software solutions for the synthesis of monitoring clusters, including information security and information counteraction systems. The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. The need to use service monitoring plays an important role in the cloud computing industry, especially for SLA/QoS assessment, as the application or service may experience problems even if the virtual machines on which the work is taking place appear to be operational. This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. The obtained results made it possible to develop mathematical models of parallel DStreams from sources processed in a cloud cluster via Hadoop technology using the micro-batch architecture of the Spark Streaming module. These models take into account the flow of requests for maintenance from sources of different services, on the one hand, and, on the other hand, the needs of services in bit rate, taking into account the multichannel traffic of sources of various services. At the same time, analytical relations are obtained to calculate the required performance of the Hadoop cluster at a given value of the probability of batch loss.

A Fast Algorithm for Classifying Seismic Events Using Distributed Computations in Apache Spark Framework

The main ideas of the development of the software implementation of an algorithm for the fast automatic classification of seismic signals based on diagnostic patterns are described. The process of adaptation and integration of this implementation into the distributed computations system Apache Spark is described in detail. A software solution for the preliminary processing of the signals and optimization of the mathematical model for parallel computations using broadcast variables is presented. Performance tests for the classification algorithm on a set of day-long signals are carried out. The execution time of the algorithm in the context of massively parallel computations was reduced tenfold compared with the sequential execution.

Special Section on the Fiftieth Annual ACM Symposium on Theory of Computing (STOC 2018)

This issue of SICOMP contains 10 specially selected papers from the Fiftieth Annual ACM Symposium on Theory of Computing, otherwise known as STOC 2018, held June 25 to 29 in Los Angeles, California...

Complete simulation of automata networks

Consider a finite set A and n≥1. We study complete simulation of transformations of An, also known as automata networks. For m≥n, a transformation of Am is n-complete of size m if it may simulate every transformation of An by updating one register at a time. Using tools from memoryless computation, we establish that there is no n-complete transformation of size n, but there is one of size n+1. By studying various constructions, we conjecture that the maximal time of simulation of any n-complete transformation is at least 2n. We also investigate the time and size of sequentially n-complete transformations, which may simulate every finite sequence of transformations of An. Finally, we show that there is no n-complete transformation updating all registers in parallel, but there exists one updating all but one register in parallel. This illustrates the strengths and weaknesses of sequential and parallel models of computation.

Программная поддержка модели BSF

The paper is devoted to the software support of the parallel computation model BSF (Bulk Synchronous Farm) intended to iterative algorithms with high computational complexity, which developed for multiprocessor systems with distributed memory of exaflops performance level. Software support includes parallel BSF-skeleton and BSF-Studio web-application. The definition and classification of parallel skeletons are given. The logic and file structure of new BSF-skeleton are described. The BSF-skeleton is a collection of C++ source code files used for BSF-programs development. A detailed description of the design and implementation of the BSF-Studio web-application, which is a visual constructor of BSF-programs, is given. BSF-Studio provides compilation and execution of program code.

Coarse grained parallel quantum genetic algorithm for reconfiguration and service restoration of electric power networks

In this paper, a Coarse Grained Parallel Quantum Genetic Algorithm (CGPQGA) is proposed to solve the network reconfiguration and restoration problems in distribution networks with the objective of reducing network losses, balancing load and improving the quality of voltage in the system. Based on t he parallel evolutionary concept and the insights of quantum theory, a model of parallel quantum computation was simulated. In this frame, there are some demes (sub-populations) and some universes (groups of populations), which are structured in super star-shaped topologies. A new migration scheme based on penetration theory is developed to control both the migration rate and direction adaptively between demes and a coarse grained quantum crossover strategy is devised among universes. The proposed approach is tested on 33-bus distribution networks in two cases the first case without distributed generators and the second case with distributed generators, with the aim of minimizing the losses of reconfigured network, and minimize number of operated switches, deviation of bus voltages, and loading of transformer (power loss) where the choice of the switches to be opened is based on the calculation of voltages at the system buses, real and reactive power flow through lines, real power losses and voltage deviations, using distribution load flow (DLF) program. Simulation results prove the effectiveness of the proposed methodology in solving the current challenges in this phase.

Обзор моделей параллельных вычислений

This survey aims to present the state of the art in analytic parallel computation models, providing sufficiently detailed descriptions of particularly noteworthy efforts. Such models allow predicting the computation time, speedup, efficiency and scalability of parallel algorithms for various target multiprocessor platforms. Modeling the cost of computations and communications in multiprocessor systems is an important and challenging problem. It provides insights into the design of the parallel algorithms for optimization of their deployment in the increasingly complex high-performance computing. The survey shows the evolution of parallel computing models inspired by the evolution of multiprocessor systems, from single-level models with shared memory to multi-level hierarchical models with distributed memory, which correspond to multicore clusters. The review concludes with prospective directions for further research in the area of developing mathematical models for parallel computing.

Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches.

Spatiotemporally continuous global river discharge estimates across the full spectrum of stream orders are vital to a range of hydrologic applications, yet they remain poorly constrained. Here we present a carefully designed modeling effort (Variable Infiltration Capacity land surface model and Routing Application for Parallel computatIon of Discharge river routing model) to estimate global river discharge at very high resolutions. The precipitation forcing is from a recently published 0.1° global product that optimally merged gauge‐, reanalysis‐, and satellite‐based data. To constrain runoff simulations, we use a set of machine learning‐derived, global runoff characteristics maps (i.e., runoff at various exceedance probability percentiles) for grid‐by‐grid model calibration and bias correction. To support spaceborne discharge studies, the river flowlines are defined at their true geometry and location as much as possible—approximately 2.94 million vector flowlines (median length 6.8 km) and unit catchments are derived from a high‐accuracy global digital elevation model at 3‐arcsec resolution (~90 m), which serves as the underlying hydrography for river routing. Our 35‐year daily and monthly model simulations are evaluated against over 14,000 gauges globally. Among them, 35% (64%) have a percentage bias within 20% (50%), and 29% (62%) have a monthly Kling‐Gupta Efficiency 0.6 (0.2), showing data robustness at the scale the model is assessed. This reconstructed discharge record can be used as a priori information for the Surface Water and Ocean Topography satellite mission's discharge product, thus named “Global Reach‐level A priori Discharge Estimates for Surface Water and Ocean Topography”. It can also be used in other hydrologic applications requiring spatially explicit estimates of global river flows.

Parallel computational building-chain model for rapid urban-scale energy simulation

As buildings consume substantial amounts of energy, researchers and decision makers are giving more attention to urban-scale energy assessment and design. In fact, researchers have developed many building-energy modeling and simulation tools that are regarded as effective for building designers and facility managers. However, few models have been found suitable for urban-scale efficient building design and planning. Indeed, to carry out urban-scale energy modeling and simulation, it is necessary to possess a comprehensive understanding of interactions among building groups as well as huge computational resources. To develop a more efficient and reliable simulation model, this study proposes a parallel computational building-chain (PCBC) model. This PCBC model aims to simplify building interactions and implement efficient multi-thread computations. It can decompose large-scale building groups into inter-connected building units by defining the thermal and shading boundary conditions of buildings in a neighborhood. By coupling individual buildings’ simulated energy consumption, the urban energy dynamics can be reconstructed. To validate the proposed method, researchers examined a sample urban-building group with 410 buildings. Compared with the conventional integrated Whole City model, the proposed method achieved nearly the same outputs with reduced computation time. With an increase in the simulation scale, computational efficiency can be improved in the future.

Reducing partition skew on MapReduce: an incremental allocation approach

MapReduce, a parallel computational model, has been widely used in processing big data in a distributed cluster. Consisting of alternate map and reduce phases, MapReduce has to shuffle the intermediate data generated by mappers to reducers. The key challenge of ensuring balanced workload on MapReduce is to reduce partition skew among reducers without detailed distribution information on mapped data. In this paper, we propose an incremental data allocation approach to reduce partition skew among reducers on MapReduce. The proposed approach divides mapped data into many micro-partitions and gradually gathers the statistics on their sizes in the process of mapping. The micropartitions are then incrementally allocated to reducers in multiple rounds. We propose to execute incremental allocation in two steps, micro-partition scheduling and micro-partition allocation. We propose a Markov decision process (MDP) model to optimize the problem of multiple-round micropartition scheduling for allocation commitment. We present an optimal solution with the time complexity of O(K · N2), in which K represents the number of allocation rounds and N represents the number of micro-partitions. Alternatively, we also present a greedy but more efficient algorithm with the time complexity of O(K · N ln N). Then, we propose a minmax programming model to handle the allocation mapping between micro-partitions and reducers, and present an effective heuristic solution due to its NP-completeness. Finally, we have implemented the proposed approach on Hadoop, an open-source MapReduce platform, and empirically evaluated its performance. Our extensive experiments show that compared with the state-of-the-art approaches, the proposed approach achieves considerably better data load balance among reducers as well as overall better parallel performance.

Исследование масштабируемости алгоритма Чиммино для решения систем линейных неравенств на кластерных вычислительных системах

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. 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.

Sequential Spiking Neural P Systems with Local Scheduled Synapses without Delay

Spiking neural P systems with scheduled synapses are a class of distributed and parallel computational models motivated by the structural dynamism of biological synapses by incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In this work, we consider the family of spiking neural P systems with scheduled synapses working in the sequential mode: at each step the neuron(s) with the maximum/minimum number of spikes among the neurons that can spike will fire. The computational power of spiking neural P systems with scheduled synapses working in the sequential mode is investigated. Specifically, the universality (Turing equivalence) of such systems is obtained.