Parallel Environment Research Articles

Enhancing Diesel Engine Performance and Reducing Emissions Using Binary Biodiesel Fuel Blend

Fossil fuel consumption provides a negative impact on the human health and environment in parallel with the decreased availability of this valuable natural resource for the future generations to use as a source of chemical energy for all applications in energy, power, and propulsion. The diesel fuel consumption in the transport sector is higher than the gasoline in most developing countries for reasons of cost and economy. Biodiesel fuel offers a good replacement for diesel fuel in compression ignition (CI) diesel engines. Earlier investigations by the authors revealed that a blend of 70% amla seed oil biodiesel and 30% eucalyptus oil (AB70EU30) is the favorable alternative renewable fuel blend that can be used as a fuel in diesel engines. With any fuel, air/fuel mixing and mixture preparation impact efficiency, emissions, and performance in CI engines. Minor adjustments in engine parameters to improve air/fuel mixing and combustion are deployable approaches to achieve good performance with alternative fuel blends in CI engines. This paper provides the role of a minor modification to engine parameters (compression ratio, injection timing, and injection pressure) on improved performance using the above mixture of binary fuel blends (AB70EU30). The results showed that the use of AB70EU30 in modified engine resulted in higher brake thermal efficiency and lower brake specific fuel consumption compared to normal diesel for improved combustion that also resulted in very low tailpipe emissions.

Individuals Identification Based on Palm Vein Matching under a Parallel Environment

Biometric identification and verification are essential mechanisms in modern society. Palm vein recognition is an emerging biometric technique, which has several advantages, especially in terms of security against forgery. Contactless palm vein systems are more suitable for real-world applications, but two of the major challenges of the state-of-the-art contributions are image deformations and time efficiency. In the present work, we propose a new method for palm vein recognition by combining DAISY descriptor and the Coarse-to-fine PatchMatch (CPM) algorithm in a parallel matching process. Our proposal aims at providing an effective and efficient technique to obtain similarity of palm vein images considering their displacements as discriminatory information. Extensive evaluation on three publicly available databases demonstrates that the discriminability of the proposed approach reaches the state-of-the-art results while it is considerably superior in time efficiency.

Lanczos-boosted numerical linked-cluster expansion for quantum lattice models.

Numerical linked-cluster expansions allow one to calculate finite-temperature properties of quantum lattice models directly in the thermodynamic limit through exact solutions of small clusters. However, full diagonalization is often the limiting factor for these calculations. Here we show that a partial diagonalization of the largest clusters in the expansion using the Lanczos algorithm can be as useful as full diagonalization for the method while mitigating some of the time and memory issues. As test cases, we consider the frustrated Heisenberg model on the checkerboard lattice and the Fermi-Hubbard model on the square lattice. We find that our approach can surpass state of the art in performance in a parallel environment.

Active learning to detect DDoS attack using ranked features

Network traffic classification to detect DDoS attacks is challenging in the context of high-speed networks. In this paper, we discuss the need for distributed feature selection in intrusion detection systems using parallel computing. This paper presents a parallel cumulative ranker algorithm to rank the attributes of a dataset for cost-effective classification of network traffic. We use MIT-DARPA, CAIDA, ISCX-IDS and TU-DDoS datasets to validate our method. Our feature ranking algorithm on large datasets (50,000-1,000,000 instances) finds best possible features from the above mentioned datasets and gives high accuracy (92%-97%) in a parallel environment, which takes significantly less time (71%-85% lower) than a sequential environment. We also discuss the importance of active learning to select appropriate instances by an expert module in an unsupervised way to train an SVM binary classifier for detection of DDoS attack traffic. Our approach selects small batches of training samples from a dataset to yield classification of network traffic with high accuracy. Our approach on large data provides better accuracy in classification with fewer training samples. A case study looks into the detection of intrusion in power systems.

Flexible and practical parallel implementation for coupled elastoplastic geomechanics and non-isothermal flow

We develop a parallel code for coupled non-isothermal flow and elastoplastic geomechanics. We mainly focus on flexible and pragmatic parallel code development and its scalability. Specifically, beginning with uncoupled individual serial codes of non-isothermal flow and geomechanics, we primarily implement MPI (Message Passing Interface) for matrix assembly and parallel solvers, particularly using the PETSc library codes while using OpenMP (Open Multi-Processing) for other miscellaneous subroutines to prevent significant overheads. We consider various matrix decomposition schemes for preconditioning parallel solvers as well as assigning computation loads to different CPU cores. For the coupled simulation, we take the fixed-stress sequential method, which allows the coupling between two simulators with least efforts of code development. As a result, the parallel code can easily be developed within a short period of time.In numerical experiments, we take more than one million cells to investigate parallel performance. For both flow and geomechanics, the parallelization largely reduces the overall simulation execution time with scalable speedups. The matrix decomposition methods have effects on execution time and solver performance, which implies that appropriate matrix decomposition is important for computational efficiency and scalability. We also find that the scalability of the parallel coupled simulator by the sequential method can honor the scalability of individual parallel simulators. Still, plasticity can cause imbalance in the parallel environment of geomechanics simulation.

VPPE: A Novel Visual Parallel Programming Environment

Parallel programming continues to be a challenging task despite the many advances in parallel architectures and their wide availability in the cloud. The need both to partition the workload among various processing elements and to specify communication between them to share code and data, and to coordinate their tasks, requires from the developer a deep understanding of the problem, the parallel architecture and the programming language used in order to develop efficient parallel applications. This problem can be reduced significantly through the use of visual programming languages to hide most aspects related to the specification of communication and processes management. This paper presents VPPE, a novel Visual Parallel Programming Environment that allows developers to program parallel applications through organising workflows of interconnected icons. VPPE is a cloud environment that supports icons for specifying: I/O operations, workflow organisation, communication, and processing. Processing computing patterns supported so far include Single Program Multiple Data, Multiple Program Multiple Data, Pipeline, and Master–Slave. The paper highlights the design of VPPE based on a context-free graph grammar, its current implementation based on Java-MPI, its use in developing various parallel applications, and its evaluation compared to Java-MPI text-based programming.

AUGMENTED ANNOTATIONS: INDOOR DATASET GENERATION WITH AUGMENTED REALITY

Abstract. The proliferation of machine learning applied to 3D computer vision tasks such as object detection has heightened the need for large, high-quality datasets of labeled 3D scans for training and testing purposes. Current methods of producing these datasets require first scanning the environment, then transferring the resulting point cloud or mesh to a separate tool for it to be annotated with semantic information, both of which are time consuming processes. In this paper, we introduce Augmented Annotations, a novel approach to bounding box data annotation that solves the scanning and annotation processes of an environment in parallel. Leveraging knowledge of the user’s position in 3D space during scanning, we use augmented reality (AR) to place persistent digital annotations directly on top of indoor real world objects. We test our system with seven human subjects, and demonstrate that this approach can produce annotated 3D data faster than the state-of-the-art. Additionally, we show that Augmented Annotations can also be adapted to automatically produce 2D labeled image data from many viewpoints, a much needed augmentation technique for 2D object detection and recognition. Finally, we release our work to the public as an open-source iPad application designed for efficient 3D data collection.

Scaling Up SLIC Superpixels Using a Tile-Based Approach

Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer’s available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method’s feasibility.

Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

Abstract machines, optimal reduction, and streams

AbstractIn this paper, we propose and explore a new approach to abstract machines and optimal reduction via streams, infinite sequences of elements. We first define a sequential abstract machine capable of performing directed virtual reduction (DVR) and then we extend it to its parallel version, whose equivalence is explained through the properties of DVR itself. The result is a formal definition of the λ-calculus interpreter called Parallel Environment for Lambda Calculus Reduction (PELCR), a software for λ-calculus reduction based on the Geometry of Interaction. In particular, we describe PELCR as a stream-processing abstract machine, which in principle can also be applied to infinite streams.

Approximate Green's Function Coupled Cluster Method Employing Effective Dimension Reduction.

The Green's function coupled cluster (GFCC) method, originally proposed in the early 1990s, is a powerful many-body tool for computing and analyzing the electronic structure of molecular and periodic systems, especially when electrons of the system are strongly correlated. However, in order for the GFCC to become a method that may be routinely used in the electronic structure calculations, robust numerical techniques and approximations must be employed to reduce its extremely high computational overhead. In our recent studies, it has been demonstrated that the GFCC equations can be solved directly in the frequency domain using iterative linear solvers, which can be easily distributed in a massively parallel environment. In the present work, we demonstrate a successful application of model-order-reduction (MOR) techniques in the GFCC framework. Briefly speaking, for a frequency regime of interest that requires high-resolution descriptions of spectral function, instead of solving the GFCC linear equation of full dimension for every single frequency point of interest, an efficiently solvable linear system model of a reduced dimension may be built upon projecting the original GFCC linear system onto a subspace. From this reduced order model is obtained a reasonable approximation to the full dimensional GFCC linear equations in both interpolative and extrapolative spectral regions. Here, we show that the subspace can be properly constructed in an iterative manner from the auxiliary vectors of the GFCC linear equations at some selected frequencies within the spectral region of interest. During the iterations, the quality of the subspace, as well as the linear system model, can be systematically improved. The method is tested in this work in terms of the efficiency and accuracy of computing spectral functions for some typical molecular systems such as carbon monoxide, 1,3-butadiene, benzene, and adenine. To reach the same level of accuracy as that of the original GFCC method, the application of MOR in the GFCC method is able to significantly lower the original computational cost for the aforementioned molecules in designated frequency regimes. As a byproduct, the reduced order model obtained by this method is found to provide a high-quality initial guess, which improves the convergence rate for the existing iterative linear solver.

Multi-agent large-scale parallel crowd simulation with NVRAM-based distributed cache

This paper presents the architecture, main components and performance results for a parallel and modular agent-based environment aimed at crowd simulation. The environment allows to simulate thousands or more agents on maps of square kilometers or more, features a modular design and incorporates non-volatile RAM (NVRAM) with a fail-safe mode that can be activated to allow to continue computations from a recently analyzed state in case of a failure. We show results for an evacuation scenario for an area of up to 6 km2 in a district of Gdansk, Poland, performed on two clusters, one with hardware simulation of NVRAM. We have shown a very small overhead of using NVRAM compared to the RAM only solution and an overhead of 20% with the fail-safe mode on using NVRAM, shown up to 30 000 agents and up to 25 000 iterations of the simulation. We also show the benefit of using NVRAM for file synchronization with a slow growth of the execution time while increasing the map size. We then present how the frequency of visualization affects execution time and very good scaling of the proposed solution in a cluster environment for more than 650 processes and 60 000 agents.

Parallel fast-neighbor-searching and communication strategy for particle-based methods

PurposeThis paper aims to develop a parallel fast neighbor search method and communication strategy for particle-based methods with adaptive smoothing-length on distributed-memory computing systems.Design/methodology/approachWith a multi-resolution-based hierarchical data structure, the parallel neighbor search method is developed to detect and construct ghost buffer particles, i.e. neighboring particles on remote processor nodes. To migrate ghost buffer particles among processor nodes, an undirected graph is established to characterize the sparse data communication relation and is dynamically recomposed. By the introduction of an edge coloring algorithm from graph theory, the complex sparse data exchange can be accomplished within optimized frequency. For each communication substep, only efficient nonblocking point-to-point communication is involved.FindingsTwo demonstration scenarios are considered: fluid dynamics based on smoothed-particle hydrodynamics with adaptive smoothing-length and a recently proposed physics-motivated partitioning method [Fu et al., JCP 341 (2017): 447-473]. Several new concepts are introduced to recast the partitioning method into a parallel version. A set of numerical experiments is conducted to demonstrate the performance and potential of the proposed parallel algorithms.Originality/valueThe proposed methods are simple to implement in large-scale parallel environment and can handle particle simulations with arbitrarily varying smoothing-lengths. The implemented smoothed-particle hydrodynamics solver has good parallel performance, suggesting the potential for other scientific applications.

Quality-driven early stopping for explorative cluster analysis for big data

Data analysis has become a critical success factor for companies in all areas. Hence, it is necessary to quickly gain knowledge from available datasets, which is becoming especially challenging in times of big data. Typical data mining tasks like cluster analysis are very time consuming even if they run in highly parallel environments like Spark clusters. To support data scientists in explorative data analysis processes, we need techniques to make data mining tasks even more efficient. To this end, we introduce a novel approach to stop clustering algorithms as early as possible while still achieving an adequate quality of the detected clusters. Our approach exploits the iterative nature of many cluster algorithms and uses a metric to decide after which iteration the mining task should stop. We present experimental results based on a Spark cluster using multiple huge datasets. The experiments unveil that our approach is able to accelerate the clustering up to a factor of more than 800 by obliterating many iterations which provide only little gain in quality. This way, we are able to find a good balance between the time required for data analysis and quality of the analysis results.

Parallel membership queries on very large scientific data sets using bitmap indexes

SummaryMany scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating‐point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word‐Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

A dynamic spring element model for the prediction of longitudinal failure of polymer composites

A spring element model that takes into account the dynamic effects associated with fibre failure in composite materials is presented. The model is implemented in a parallel environment to allow a better performance in the prediction of the complex mechanisms associated with longitudinal tensile failure. The model is used to identify the changes in the stress fields around a broken fibre representing fibre failure as a dynamic phenomenon. In light of these changes in the stress fields, the cluster formation and failure development is analysed and the results are compared with the static spring element model. It is observed that the stress redistribution around a broken fibre is strongly dependent on the dynamic effects and it varies with the material in study, specially if the matrix is considered linear elastic. The changes in local stress redistribution are seen to affect the materials tensile behaviour and cluster formation, being this changes higher for a material with an elastic matrix.

A parallel Voronoi-based approach for mesoscale simulations of cell aggregate electropermeabilization

We introduce a numerical framework that enables unprecedented direct numerical studies of the electropermeabilization effects of a cell aggregate at the meso-scale. Our simulations qualitatively replicate the shadowing effect observed in experiments and reproduce the time evolution of the impedance of the cell sample in agreement with the trends observed in experiments. This approach sets the scene for performing homogenization studies for understanding the effect of tissue environment on the efficiency of electropermeabilization. We employ a forest of Octree grids along with a Voronoi mesh in a parallel environment that exhibits excellent scalability. We exploit the electric interactions between the cells through a nonlinear phenomenological model that is generalized to account for the permeability of the cell membranes. We use the Voronoi Interface Method (VIM) to accurately capture the sharp jump in the electric potential on the cell boundaries. The case study simulation covers a volume of (1mm)3 with more than 27,000 well-resolved cells with a heterogeneous mix of morphologies that are randomly distributed throughout a spheroid region.

Image Encryption Based on Parallel Algorithm via Zigzag Manner with a New Chaotic System

Information safety has become a vital issue and data encryption and decryption have lately been vastly researched and developed. There is a need for a powerful encryption and decryption which is very tough to crack, so cryptography plays a major role to fulfill these demands. Moreover, parallel algorithm is rapidly becoming cost and resource-efficient as a result of a unified approach to solve computer problems and wide-ranging problems encountered in applied sciences and engineering applications. In this paper, a parallel environment has been utilized to construct a new encryption system, based on involving the so-called 'zig-zag' ordering that is used in JPEG data compression. A new chaotic system of three dimensions will be produced to eliminate the common encryption systems problems.

Development and Deployment of an Open, Modular, Near-Real-Time Patient Monitor Datastream Conduit Toolkit to Enable Healthcare Multimodal Data Fusion in a Live Emergency Department Setting for Experimental Bedside Clinical Informatics Research

Healthcare providers rely on complex biomedical devices to assess, treat, and monitor patients. Ongoing research efforts are attempting to generate and implement better algorithms and mechanisms to ensure the early, accurate, automated, and clinically meaningful recognition of patterns and changes in patient health and pathology. Effecting such evolutionary advances in patient monitoring will likely require large collections of high-resolution physiologic parameter datasets from a broad spectrum of patients. As part of a research program to scientifically improve patient monitoring (with a focus on alarm fatigue mitigation), investigators developed the Medical Technology Interface-Open/Research toolkit with modular conduit components that provide the following capabilities: 1) access to select bedside monitor physiologic signals in real-world clinical settings for near-real-time acquisition, storage, and export of high-resolution patient datastreams in a portable format (.json); 2) establishment of a safe, parallel test environment at the bedside for experimental datastream analyses in a research framework. Deployment and interfacing of toolkit elements with off-the-shelf software solutions in a live emergency department setting enabled the construction of a bedside clinical informatics (BCI) research pipeline infrastructure that featured 1) indexing, search/query, and retrieval of datastreams for sophisticated analyses, experimental processing, and algorithm development; and 2) dataset visualization for expert adjudication of datastream interpretability, alarm clinical significance and severity, and experimental algorithm performance. In order to help institute a collaborative biomedical engineering research resource, this article shares details of the active ED BCI data pipeline and presents preliminary examples of ongoing multimodal data fusion applications.

Green fuzzy parallel machine scheduling with sequence-dependent setup in the plastic moulding industry

Job scheduling has always been a challenging task for production managers. It is of special importance if it involves multi-objectives and parallel machines. In this research, our direction is largely motivated by the uncertainty emerged in production systems, where processing times of jobs and setup times of machines are not fixed and they are subject to some uncertainties. Our paper presents a mixed-integer goal programming model considering fuzzy approach for parallel machines environments with separable jobs, sequence-dependent setup times and hazardous waste. Two fuzzy objectives are considered in the model to minimise the total tardiness and the total hazardous waste. Since the model is too complex to be solved using exact methods, we proposed a solution method based on genetic algorithm. To set the input parameters of the solution algorithm, Taguchi method is used. The results of the computational experiments indicate that the proposed algorithm is a proficient method and has efficiently found the optimal solutions for both objective functions.