Parallel Machine Model Research Articles

Tunnel: Parallel-inducing sort for large string analytics

The suffix array is a crucial data structure for efficient string analysis. Over the course of twenty-six years, sequential suffix array construction algorithms have achieved O(n) time complexity and in-place sorting. In this paper, we present the Tunnel algorithm, the first large-scale parallel suffix array construction algorithm with a time complexity of Onp based on the parallel random access machine (PRAM) model. The Tunnel algorithm is built on three key ideas: dividing the problem of size O(n) into p sub-problems of reduced size Onp by replacing long suffixes with shorter prefixes of size at most a constant D; introducing a Tunnel mechanism to efficiently induce the order of a set of suffixes with long common prefixes; developing a strategy to transform a partially ordered suffix set into a total order relation by iteratively applying the Tunnel inducing method. We provide a detailed description of the algorithm, along with a thorough analysis of its time and space complexity, to demonstrate its correctness and efficiency. The proposed Tunnel algorithm exhibits scalable performance, making it suitable for large string analytics on large-scale parallel systems.

Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes.

Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (ts) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of ts and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, ts/Y, Wb/D, and ts/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, ts/Y, W/D, and ts/D are considered for model development. It is found that models that incorporate sediment bed thickness (ts) provide better results than those whichuse deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized ts/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.

Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture

The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neural network implementations have been observed to train to nearly 80% training accuracy in 200 epochs. In effect, therefore, the comparison is on the time taken to complete the training epochs.

Results of Parallel-Machine Scheduling Model with Maintenance Activity considering Time-Dependent Deterioration, Delivery Times, and Resource Allocation

This paper investigates parallel-machine scheduling models with maintenance activity, delivery times, time-dependent deterioration, and resource allocation. We consider two forms of the problem: the first is to minimize the sum of total completion times, total machine loads, the total absolute deviation of job completion times, and the total resource allocation; the second is to minimize the sum of total waiting times, total machine loads, the total absolute deviation of job waiting times, and the total resource allocation. The problems are proved to be solvable in polynomial time.

LabVIEW based robust cascade predictive model for evaluating cancer prognosis

Cancer is a disease that is found in many forms. Early diagnosis process significantly affects follow-up of the disease. As in other diseases, it is important to classify the data in cancer cases to determine whether the person belongs to healthy-patient or high-low risk groups. For this purpose, machine learning based on artificial intelligence can be used as a very effective method to follow both the progress and the treatment response process of such diseases and to reveal important features of data sets. In this publication, breast cancer diagnosis was carried out using Principal Component Analysis-Support Vector Machine (PCA-SVM) and proposed parallel Principal Component Analysis-Linear Discriminant Analysis-Support Vector Machine (PCA-LDA-SVM) model classifier algorithms, by LabVIEW. LabVIEW, known as Virtual Instrument (VI), is a graphical programming language. The durableness of the used algorithms is analyzed using accuracy, sensitivity, specificity, rand index, False Positive Rate (FPR), False Discovery Rate (FDR), False Negative Rate (FNR), Negative Predictive Value (NPV), Matthews Correlation Coefficient (MCC) parameters and status detection. The obtained results are compared with each other. After training, of the 140 data used in the test set, 130 were used for the test performance analysis and 10 data were used for the status determination of the newly entered data. Performance analysis has been examined for Polynomial and Gaussian kernel functions. The proposed parallel model provides improvement especially for the Polynomial kernel function. With the proposed model, an increase in classification accuracy was observed in the test phase compared to PCA-SVM, and it was observed that 10 data used for status determination were correctly classified.

Analysis of Collision Probability in a Parallel Machines Model When the Number of Jobs Is Constrained

This paper analyses collision probability in a production line. Collision probability is the probability of a collision occurring between jobs in a production line. One element that determines collision probability is the time interval between materials which are being fed into a production line. When the time interval is shortened, the probability of a collision occurring increases. However, in order to reduce production completion time, this time interval should be as short as possible. When determining the appropriate time interval, evaluating collision probability for a given time interval is important. Methods to compute collision probability in an in-line and a parallel machines model already exist. Moreover, in an in-line machines model, an approximate formula of collision probability has also been presented. In a parallel machines model, there are no results on analysis of collision probability. This paper shows a theoretical formula of collision probability in a parallel machines model when the number of jobs is constrained. Further, when the processing time follows an Erlang distribution, we show that this theoretical formula of collision probability can be described as a closed form. In addition, when the processing time follows an exponential distribution,we show a concrete closed form of collision probability. Finally, from computational experimentation, we compare the results obtained from the closed form presented with a known simulation method.

A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

A Framework for Computing Collision Probability in a Parallel Machines Model

A Framework for Computing Collision Probability in a Parallel Machines Model

Parallel Turing Machine, a Proposal

We have witnessed the tremendous momentum of the second spring of parallel computing in recent years. But, we should remember the low points of the field more than 20 years ago and review the lesson that has led to the question at that point whether “parallel computing will soon be relegated to the trash heap reserved for promising technologies that never quite make it” in an article entitled “the death of parallel computing” written by the late Ken Kennedy — a prominent leader of parallel computing in the world. Facing the new era of parallel computing, we should learn from the robust history of sequential computation in the past 60 years. We should study the foundation established by the model of Turing machine (1936) and its profound impact in this history. To this end, this paper examines the disappointing state of the work in parallel Turing machine models in the past 50 years of parallel computing research. Lacking a solid yet intuitive parallel Turing machine model will continue to be a serious challenge in the future parallel computing. Our paper presents an attempt to address this challenge by presenting a proposal of a parallel Turing machine model. We also discuss why we start our work in this paper from a parallel Turing machine model instead of other choices.

A combined parallel genetic algorithm and support vector machine model for breast cancer detection

A combined parallel genetic algorithm and support vector machine model for breast cancer detection

A parallel computational model based on mobile agents for high performance computing

Abstract The goal of this paper is to present a new massively parallel virtual machine model, designed for parallel and high performance computing on distributed systems. The proposed model allows us to build a polymorphic grid computing assigned to solve fine grained parallel problems over different well known, SIMD (Single Instruction Multiple Data), SPMD (Single Program Multiple Data), MIMD (Multiple Instruction Multiple Data) ) and MPMD (Multiple Program Multiple Data) machine structures. This model is built using dynamic distributed Virtual Processors Units (VPU). Each VPU corresponds to a mobile agent deployed in a physical processing unit. Each physical processing unit is assigned to a node of the considered distributed system. These units may be heterogeneous machines, they are characterized by their physical performance indices. The proposed model is able to include different computing unit technologies, such as supercomputers, simple desktop or smart phones etc. The VPUs are designed to communicate with each other asynchronously by exchanging, in local or remote manner, the ACL messages (Agent Communication Language) containing data, instructions or any task to be performed. In this model a special agent is designed to represent the host of the parallel virtual machine and to manage the following activities: the life cycle of the VPUs, the load balancing control and the parallel programs to run. In this model VPUs can also use a virtual shared memory represented by hierarchical mobile agents. All the properties offered to the proposed model, are easily designed thanks to the flexibility and the mobility of the multi agent systems. This leads to a strong computation model for high perform-

MapReduce-based Parallel Learning for Large-scale Remote Sensing Images

Machine learning applied to large-scale remote sensing images shows inadequacies in computational capability and storage space. To solve this problem, we propose a cloud computing-based scheme for learning remote sensing imag- es in a parallel manner: (1) a hull vector-based hybrid parallel support vector machine model (HHB-PSVM) is proposed. It can substantially improve the efficiency of training and prediction for the large-scale samples while guaranteeing classi- fication accuracy. (2) The MapReduce model is used to achieve parallel extraction of the classification features for the remote sensing images, and the MapReduce-based HHB-PSVM model (MapReduce-HHB-PSVM) is used to implement the training and prediction for large-scale samples. (3) MapReduce-HHB-PSVM is applied to land use classification, ena- bling various types of land use to be classified more efficiently by using fused hyperspectral images. Experimental results show that MapReduce-HHB-PSVM can substantially improve classification efficiency of large-scale remote sensing im- ages while guaranteeing classification accuracy, and it can promote the machine interpretation of ground objects infor- mation extracted from the large-scale remote sensing images to be conducted intelligently.

Scheduling Simple Linear Deteriorating Jobs with Rejection

We consider the problems of scheduling deteriorating jobs with release dates on a single machine (parallel machines) and jobs can be rejected by paying penalties. The processing time of a job is a simple linear increasing function of its starting time. For a single machine model, the objective is to minimize the maximum lateness of the accepted jobs plus the total penalty of the rejected jobs. We show that the problem is NP-hard in the strong sense and presents a fully polynomial time approximation scheme to solve it when all jobs have agreeable release dates and due dates. For parallel-machine model, the objective is to minimize the maximum delivery completion time of the accepted jobs plus the total penalty of the rejected jobs. When the jobs have identical release dates, we first propose a fully polynomial time approximation scheme to solve it. Then, we present a heuristic algorithm for the case where all jobs have to be accepted and evaluate its efficiency by computational experiments.

A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model

The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy.

P systems with proteins on membranes characterize PSPACE

The paper studies algorithmic properties of operations with membrane proteins modeled within the framework of membrane systems (also called P systems). Membrane systems are biologically inspired models of parallel and distributed computing based on the information processing in cells and cellular membranes. We show that the computational potential of P systems with proteins on membranes is equivalent to that of parallel computing models as the alternating Turing machine or the PRAM. These abstract machines characterize by their polynomial time-bounded computations the class PSPACE, and simultaneously they serve as idealized models of real parallel machines. Therefore, this and other related results suggest the existence of a homology between the potential of silicon and biological parallel information processing.

On single-walk parallelization of the job shop problem solving algorithms

New parallel objective function determination methods for the job shop scheduling problem are proposed in this paper, considering makespan and the sum of jobs execution times criteria, however, the methods proposed can be applied also to another popular objective functions such as jobs tardiness or flow time. Parallel Random Access Machine (PRAM) model is applied for the theoretical analysis of algorithm efficiency. The methods need a fine-grained parallelization, therefore the approach proposed is especially devoted to parallel computing systems with fast shared memory (e.g. GPGPU, General- Purpose computing on Graphics Processing Units).

Optimal solutions for a dock assignment problem with trailer transportation

This paper presents a model for a dock assignment problem, where trailers need to be assigned to gates for a given period of time for loading or unloading activities. The parking lot is used as a buffer zone. Transportation between the parking lot and the gates is performed by additional resources called terminal tractors. The problem is modeled as a three-stage flexible flow shop, where the first and the third stage share the same identical parallel machines and the second stage consists of a different set of identical parallel machines. We examine multiple integer-programming formulations for the parallel-machine model in stage two and for the three-stage flow shop and we provide extensive computational results. Our goal is to explore the limits of the instance sizes that can be solved to guaranteed optimality within acceptable running times using integer programming.

A Production Planning and Scheduling Architecture for Networked-Manufacturing System Based on Available-to-Promise

In the practical application of push-pull based production planning and scheduling architecture, the manufacturing system was found lacking of collaborative mechanism, especially for a networked-manufacturing environment, which requires each individual manufacturer interact and cooperate with each other for a collaborative manufacturing. This paper presents a production planning and scheduling architecture for networked-manufacturing system based on available-to-promise, which can effectively merge forecast-driven production activity with order-driven production activity, thus ensures the steady and prompt supply of material, and also cooperation and mutual benefit of individual manufacturer. This architecture consists of 1) an ATP-based order management and decision-making system, 2) a push-pull based multi-plant master production schedule collaboration model, 3) a pre-reactive collaborative replenishment model, 4) a production scheduling model of unrelated parallel machine and 5) the corresponding production planning and scheduling methods for each model. By combining the concept of ATP, this architecture can not only provide resource planning for networked-manufacturing system, but also offer quick response and promise to customer requests.

3D block-based medial axis transform and chessboard distance transform based on dominance

Traditionally, the block-based medial axis transform (BB-MAT) and the chessboard distance transform (CDT) were usually viewed as two completely different image computation problems, especially for three dimensional (3D) space. In fact, there exist some equivalent properties between them. The relationship between both of them is first derived and proved in this paper. One of the significant properties is that CDT for 3D binary image V is equal to BB-MAT for image V' where it denotes the inverse image of V. In a parallel algorithm, a cost is defined as the product of the time complexity and the number of processors used. The main contribution of this work is to reduce the costs of 3D BB-MAT and 3D CDT problems proposed by Wang [65]. Based on the reverse-dominance technique which is redefined from dominance concept, we achieve the computation of the 3D CDT problem by implementing the 3D BB-MAT algorithm first. For a 3D binary image of size N 3, our parallel algorithm can be run in O(log N) time using N 3 processors on the concurrent read exclusive write (CREW) parallel random access machine (PRAM) model to solve both 3D BB-MAT and 3D CDT problems, respectively. The presented results for the cost are reduced in comparison with those of Wang's. To the best of our knowledge, this work is the lowest costs for the 3D BB-MAT and 3D CDT algorithms known. In parallel algorithms, the running time can be divided into computation time and communication time. The experimental results of the running, communication and computation times for the different problem sizes are implemented in an HP Superdome with SMP/CC-NUMA (symmetric multiprocessor/cache coherent non-uniform memory access) architecture. We conclude that the parallel computer (i.e., SMP/CC-NUMA architecture or cluster system) is more suitable for solving problems with a large amount of input size.

Minimizing flow time for the worker assignment problem in identical parallel machine models using GA

This article proposes a genetic algorithm approach to minimize total flow time of a set of tasks for identical parallel machines and worker assignment to machines. A spreadsheet-based domain independent general purpose genetic algorithm (GA), an add-in to the spreadsheet software, is used to solve the problem. The paper demonstrates an adaptation of the proprietary GA software to the problem of minimizing total flow time for the worker assignment scheduling problem for identical parallel machine models. Two 100 I/P/n/m/W problems taken from Hu (Int J Adv Manuf Technol 25:1046–1052, 2005, 29:753–757, 2006). The performance of GA is superior to SPT-A/LMC approach used by Hu. As compared to Hu, the proposed GA finds optimal solution for all 100 I/P/6/3/10 problems and for 99 problems out of the 100 I/P/12/3/10 problems.