Distributed Multiprocessor Systems Research Articles

Optimizing Time Consumption for Smartphone-based Distributed Parallel Processing System

Optimizing Time Consumption for Smartphone-based Distributed Parallel Processing System

A Study on Aspect of Artificial Neural Networks for Machine Learning

Artificial Neural Network (ANN) uses the processing of the brain as a basis to develop algorithms that can be used to model complex patterns and prediction problems. In our brain, there are billions of cells called neurons, which processes information in the form of electric signals. External information/stimuli is received by the dendrites of the neuron, processed in the neuron cell body, converted to an output and passed through the Axon to the next neuron. The next neuron can choose to either accept it or reject it depending on the strength of the signal. Neural network architecture is presented as one approach to the design and implementation of intelligent control systems. Neural networks can be considered as massively parallel distributed processing systems with the potential for ever-improving performance through dynamical learning. The nomenclature and characteristics of neural networks are outlined. Two simple examples are presented to illustrate applications to control systems: one is fault isolation mapping, and the other involves optimization of a Hopfield network that defines a clock less analog-to-digital conversion. Deep learning is one of the types of artificial intelligence and machine learning; basically, deep learning behaves like a human brain, or we can say that it processes the data like the human brain. In deep learning, we collect data from different resources in different complex patterns, such as photos and text, and make predictions as well. As we are also able to automate the different tasks which require the human brain.

Construction Means of Soil Microbial Synusiologic Network Based on ANN.

With the construction of synusiologic civilization and synusiologic environmental protection entering a new era driven by data, the breadth and depth of application of the DM technique in the domain of synusiologic environmental protection are constantly strengthened. If reasonable planning is not carried out in the process of social construction, it will cause unpredictable damage to the synusiologic environment. However, traditional synusiologic planning means too much human interference, and there are still some shortcomings in accuracy and operability, which means they cannot guide synusiologic construction well. In order to analyze the contribution of the soil nutrient data to soil fertility and dig out the knowledge describing soil fertility, this paper studies the construction means of soil microbial synusiologic network by ANN. By simulating the learning, memorizing, and processing problems of human brain neurons, the artificial network establishes a parallel distributed processing system computing DMG model with a large number of connections, which can quickly acquire knowledge from the outside world and store and process it and respond to the changes in the external environment in time. According to the research in this paper, the network performance of this algorithm is 18% better than that of the traditional algorithm, and it is suitable to be widely put into practice.

FAST Design for Large-Scale Satellite Image Processing

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

II. Your Present Moment in Spacetime

This essay extends the argument begun in "Why Quantum Mechanics Makes Sense," exploring the conditions under which a physical world can define and communicate information. I argue that like the structure of quantum physics, the principles of Special and General Relativity can be understood as reflecting the requirements of a universe in which things are observable and measurable. I interpret the peculiar hyperbolic structure of spacetime not as the static, four-dimensional geometry of an unobservable "block universe", but as the background metric of an evolving web of communicated information that we, along with all our measuring instruments and recording devices, actually experience in our local "here and now." Our relativistic universe is conceived as a parallel distributed processing system, in which a common objective reality is constantly being woven out of many kinds of facts determined separately in countless local measurement-contexts.

Development of Distributed Parallel Processing System Using P2P

Development of Distributed Parallel Processing System Using P2P

DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference

Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.

Dps-MuSyQ: A Distributed Parallel Processing System for Multi-Source Data Synergized Quantitative Remote Sensing Products Producing

With the development of earth observation technologies and the construction of earth observation systems, an increasing amount of remote sensing data are being obtained. These provide the datasets required for research on remote sensing monitoring across large areas. To compensate for the shortcomings of global and large-area temporal monitoring data, synergized computing using multi-source remote sensing data can improve the accuracy and temporal resolution of remote sensing monitoring. However, remote sensing data are drawn from multiple sources and multiple scales, and have a complex structure and large volume; in addition, the nested system architecture of multi-source synergized remote sensing products makes the design of large-scale multi-source synergized remote sensing monitoring systems difficult. In this paper, we describe the design and implementation of a distributed parallel processing system for multi-source data synergized quantitative remote sensing based on a distributed cluster platform. The system integrates the algorithms normalizing more than 30 kinds of data sources and producing 40 quantitative remote sensing products. The system also connects a number of centers for satellite data, serves for several applications, and implements dynamic expansion integration for highly efficient quantitative remote sensing products. The system has produced approximately 50 TB of quantitative remote sensing products, and the application of these data to agriculture, forestry, the environment, and water conservation has resulted in very positive effects.

Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data

This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually; they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.

Multi-objective mapping of full-mission simulators on heterogeneous distributed multi-processor systems

Full-mission simulators (FMSs) are considered the most critical simulation tool belonging to the flight simulator family. FMSs include a faithful reproduction of fighter aircraft. They are used by armed forces for design, training, and investigation purposes. Due to the criticality of their timing constraints and the high computation cost of the whole simulation, FMSs need to run in a high-performance computing system. Heterogeneous distributed systems are among the leading computing platforms and can guarantee a significant increase in performance by providing a large number of parallel powerful execution resources. One of the most persistent challenges raised by these platforms is the difficulty of finding an optimal mapping of n tasks on m processing elements. The mapping problem is considered a variant of the quadratic assignment problem, in which an exhaustive search cannot be performed. The mapping problem is an NP-hard problem and solving it requires the use of meta-heuristics, and it becomes more challenging when one has to optimize more than one objective with respect to the timing constraints. Multi-objective evolutionary algorithms have proven their efficiency when tackling this problem. Most of the existent works deal with the task mapping by considering either a single objective or homogeneous architectures. Therefore, the main contribution of this paper is a framework based on the model-driven design paradigm allowing us to map a set of intercommunicating real-time tasks making up the FMS model onto the heterogeneous distributed multi-processor system model. We propose a multi-objective approach based on the well-known optimization algorithm “Non-dominated Sorting Genetic Algorithm-II” satisfying the tight timing constraints of the simulation and minimizing makespan, communication cost, and memory consumption simultaneously.

Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data

We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.

INTELLIGENT SOLVING MACHINES ON MODERN PARALLEL ARCHITECTURES

The paper considers the creation of intelligent solving machines and the arrangement of parallel programming in intelligent distributed multiprocessor systems based on them. There are proposed some main concepts. A system is designed for programming in the C+Graph high-level language. The ideology proposed can be considered as an efficient development of structural high-level language interpretation when applied to multi-microprocessor systems. Equipment structure of the basic version of intelligent solving machines is considered and some characteristics are discussed.

학습 시스템을 위한 빅데이터 처리 환경 구축

In order to create an environment for Apache Hadoop for parallel distributed processing system of Bigdata, by connecting a plurality of computers, or to configure the node, using the configuration of the virtual nodes on a single computer it is necessary to build a cloud fading environment. However, be constructed in practice for education in these systems, there are many constraints in terms of cost and complex system configuration. Therefore, it is possible to be used as training for educational institutions and beginners in the field of Bigdata processing, development of learning systems and inexpensive practical is urgent. Based on the Raspberry Pi board, training and analysis of Big data processin g, such as Hadoop and NoSQL is now the design and implementation of a learning system of parallel distributed processing of possible Bigdata in this study. It is expected that Bigdata parallel distributed processing system that has been implemented, and be a useful system for beginners who want to start a Bigdata and education.

Exploring parallel capabilities of an innovative numerical method for recovering image velocity vectors field

In this paper an efficient method devoted to estimate the velocity vectors field is investigated. The method is based on a quasi-interpolant operator and involves a large amount of computation. The operations characterizing the computational scheme are ideal for parallel processing because they are local, regular and repetitive. Therefore, the spatial parallelism of the process is studied to rapidly proceed in the computation on distributed multiprocessor systems. The process has shown to be synchronous, with good task balancing and requiring a small amount of data transfer.

Harmony in Linguistic Cognition

In this article, I survey the integrated connectionist/symbolic (ICS) cognitive architecture in which higher cognition must be formally characterized on two levels of description. At the microlevel, parallel distributed processing (PDP) characterizes mental processing; this PDP system has special organization in virtue of which it can be characterized at the macrolevel as a kind of symbolic computational system. The symbolic system inherits certain properties from its PDP substrate; the symbolic functions computed constitute optimization of a well-formedness measure called Harmony. The most important outgrowth of the ICS research program is optimality theory (Prince & Smolensky, 1993/2004), an optimization-based grammatical theory that provides a formal theory of cross-linguistic typology. Linguistically, Harmony maximization corresponds to minimization of markedness or structural ill-formedness. Cognitive explanation in ICS requires the collaboration of symbolic and connectionist principles. ICS is developed in detail in Smolensky and Legendre (2006a); this article is a précis of and guide to those volumes.

Development and Characterization of a Flat-Panel Detector-Based Microtomography System

In this paper, we describe the development and performance evaluation of a 3-dimentional (3-D) high-resolution x-ray microtomography (micro-CT) system. Unlike a conventional micro-CT, the developed system uses a flat-panel detector as a digital x-ray imager. The detector is a CsI:Tl (thallium-doped cesium iodide) scintillator coupled to an active-matrix photodiode array with a pixel pitch of 50 [μm]. Without geometric magnification, the spatial resolution of the detector is 7 [lp/mm] at 10 [%] of MTF (modulation-transfer function). The overall efficiency of the detector for the input x-ray signal-to-noise ratio (SNR) has been measured to be about 50 [%] with the x-ray source operating at 60 [kVp] and 1-mm-thick Al filtration. For fast 3-D cone-beam image reconstruction, the Feldkamp algorithm has been realized in a distributed parallel processing system composed of multiple personal computers. The signal and noise properties in tomograms have been measured with quantitative phantoms and the measurement results are found to conform well to the theoretical models. From the measurements, it has been also found that the spatial resolution in a tomogram is almost determined by the detector resolving power. Some high-resolution imaging results are shown to demonstrate the capability of the developed system in bio-medical and industrial applications.

Best-Case Response Times and Jitter Analysis of Real-Time Tasks

In this paper, we present a simple recursive equation and an iterative procedure to determine the best-case response times of periodic tasks under fixed-priority preemptive scheduling and arbitrary phasings. The approach is of a similar nature as the one used to determine worst-case response times (Joseph and Pandya, 1986) in the sense that where a critical instant is considered to determine the latter, we base our analysis on an optimal instant. Such an optimal instant occurs when all higher priority tasks have a simultaneous release that coincides with the completion of an execution of the task under consideration. The resulting recursive equation closely resembles the one for worst-case response times. The iterative procedure is illustrated by means of a small example. Next, we apply the best-case response times to analyze jitter in distributed multiprocessor systems. To this end, we discuss the effect of the best-case response times on completion jitter, as well as the effect of release jitter on the best-case response times. The newly derived best-case response times generally result in tighter bounds on jitter, in turn leading to tighter worst-case response time bounds.

An Adaptive Hybrid Task Partitioning, Scheduling (AHTS), and Load Balancing Strategy for Heterogeneous Distributed System

Heterogeneous Parallel Distributed Processing (PDP) systems, which exploit the aggregate power of a network of workstations (WSs) and Personal Computers (PCs) are an inexpensive alternative to the dedicated parallel supercomputing systems. As these systems are widely available in academic and industrial environments, it is becoming popular to use these computing resources to solve time consuming applications. The main problem with such type of cluster computing environment is the continuous change in the performance of individual WSs/PCs which requires an efficient task partitioning, scheduling, and load balancing to get better performance.In this paper, we investigate the problem of static and runtime tasks allocation for parallel-distributed image computing system. The PDP system has heterogeneity in processors and resources. For such system, we propose an Adaptive Hybrid Task partitioning, Scheduling (AHTS), and load balancing strategy. The investigation is examined on a manager/master and workers model of PDP system. The measured results show that, the AHTS strategy dramatically improves the performance of PDP image computing system and remedy the defects of static and Runtime Task Scheduling (RTS) strategies.

Distributed parallel processing by using the object-oriented technology in ProteinDF program for all-electron calculations on proteins

To understand electronic properties and reactivities of proteins, we developed a molecular orbital (MO) program ProteinDF based on the density functional theory with Gaussian functions. By using it, we recently performed an all-electron calculation on a typical hemoprotein, cytochrome c. ProteinDF was coded by the object-oriented language C++ to construct the complex MO program for calculations on proteins with over a thousand orbitals. This program was parallelized for distributed parallel processing systems such as workstation clusters. In this Letter, we report parallelization methods by using the object-oriented technology in ProteinDF and their efficiency.

A Recurrent Neural Network that Learns to Count

Parallel distributed processing (PDP) architectures demonstrate a potentially radical alternative to the traditional theories of language processing that are based on serial computational models. However, learning complex structural relationships in temporal data presents a serious challenge to PDP systems. For example, automata theory dictates that processing strings from a context-free language (CFL) requires a stack or counter memory device. While some PDP models have been hand-crafted to emulate such a device, it is not clear how a neural network might develop such a device when learning a CFL. This research employs standard backpropagation training techniques for a recurrent neural network (RNN) in the task of learning to predict the next character in a simple deterministic CFL (DCFL). We show that an RNN can learn to recognize the structure of a simple DCFL. We use dynamical systems theory to identify how network states reflect that structure by building counters in phase space. The work is an empirical investigation which is complementary to theoretical analyses of network capabilities, yet original in its specific configuration of dynamics involved. The application of dynamical systems theory helps us relate the simulation results to theoretical results, and the learning task enables us to highlight some issues for understanding dynamical systems that process language with counters.