Analysis of parallelism in modular flowsheet calculations

Recent progress in the algorithm as well as the processor power have made the dynamics simulation of complex kinematic chains more realistic in various fields such as human motion simulation and molecular dynamics. The computation can be further accelerated by employing parallel processing on multiple processors. In fact, parallel processing environment is becoming more affordable thanks to recent release of multiplecore processors. Although several parallel algorithms for the forward dynamics computation have been proposed in literature, there still remains the problem of automatic scheduling, or load distribution, for handling arbitrary kinematic chains on a given parallel processing environment. In this paper, we propose a method for finding the schedule that minimizes the computation time. We test the method using three human character models with different complexities and show that parallel processing on two processors reduces the computation time by 35–36%.

Biodegrading plumes in groundwater are often typified by relatively reactive zones around the fringes and less reactive zones in the core. A high degree of refinement is required at the fringes if a model is to be of use in improving the conceptual understanding of plumes. Two strategies for dealing with the potentially high computational demands are (i) parallel processing, where the workload is shared between multiple processors, and (ii) locally adaptive remeshing, where a refined area of the grid tracks the moving plume fringes through the domain. The partial differential equation toolbox, UG (Unstructured Grids) offers advanced numerical tools including adaptive remeshing, sparse matrix storage schemes, and multigrid solvers. It embraces many of these features within a parallel processing environment. This paper reports on a recent development of UG to simulate field scale reactive biogeochemistry including Monod kinetics, NAPL dissolution, mineral precipitation/dissolution and ion exchange. The non-linear multicomponent reactive transport system is solved with the fully coupled method. Test cases have been used for verification of the new capability. The paper illustrates an application to a 3D field site. It is demonstrated that both adaptive remeshing and parallel processing can improve efficiency and in turn facilitate the incorporation of a more complex set of species and reactions such that understanding of plume processes is enhanced.

A programming system has been implemented in which annotated Petri nets are used as machine-processable high-evel design representations. The nets can be used to express the parallelism and the dynamic sequential dependencies found in complex software. They can then be interactively fired to facilitate debugging of the design. The nets are processed into a procedure language, called XL/1, to which a variety of transformations are applied in order to produce more efficient programs. These programs are generated for either a serial or a parallel processing environment. Finally, the XL/1 programs may be translated into PL/I or PL/S. The serial processing versions have been compiled and run successfully, but the parallel processing versions have not yet been run in a parallel processing environment.

This paper proposes an architecture for accomplishing transport protocol processing at Gbps rates. The limitations of currently used transport protocols have been analyzed extensively in recent literature. Several benchmark studies have established the achievable throughput of ISO TP4 and TCP to be in the low Mbps range; several new protocols and implementation techniques have been proposed to achieve 100 Mbps and higher throughput rates. We briefly review some of these protocols and establish the need for a radically different approach to meet our objective. An estimate of the aggregate processing power required for Gbps throughput is developed. It is proposed that a cost effective and practical solution to the processing requirements could be based on a multi-processor system. The opportunities for parallel processing in a typical transport protocol are examined. Several alternate parallel processing approaches are examined and arguments are advanced for selecting a favored approach. A corresponding parallel processing architecture is described. Data structures used to preserve packet ordering and techniques for reducing contention in a multi-processing environment are discussed. An implementation methodology for conventional transport protocols (e.g. TP4) is outlined. Some suggestions are made for improving efficiency by making modifications to the protocol that do not compromise functionality. The performance achievable with this modified architecture is analyzed and some suggestions for further work are presented.

Current and future parallel computing model has been suggested for running and solving large-scale application problems such as climate, bio, cryptology, and astronomy, etc. Parallel computing is a form of computation in which many calculations are carried out simultaneously. And we are able to shorten the execution time of the program, as well as can extend the scale of the problem that can be solved. In this paper, we perform the actual cryptographic algorithms through parallel processing and evaluate its efficiency. Length of the key, which is stable criterion of cryptographic algorithm, judged according to the amount of complete enumeration computation. So we present a detailed procedure of DES key search cryptographic algorithms for executing of enumeration computation in parallel processing environment. And then, we did the simulation through applying to clustering system. As a result, we can measure the safety and solidity of cryptographic algorithm.

SummaryPower system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

Manipulation of terrain data for a real-time display application

An Environment for Knowledge Parallel Processing Used in Process Control

Parallel processing, neural networks and genetic algorithms

GeoFEM is solid earth simulator software, which is under development to be used by STA's super parallel computer, Earth Simulator (GS40). GeoFEM has already been used to analyze large-scale static linear problems up to 100M (100,000,000) degrees of freedom (DOF) and demonstrated high performance computing ability by parallel processing. Therefore, in the present study, we apply the same data structure and a similar parallelization method to wave propagation problems. We describe the formulation, parallelization procedure and benchmark results of the wave propagation analysis function of GeoFEM. According to the simple benchmark problem, GeoFEM demonstrated more than 98% of the rate of CPU usage performing parallel wave propagation analysis of 100M DOF problem using 1,000 PEs and also shows almost linear scalability on the Hitachi SR2201.

Massively parallel data analysis is an emerging research topic that is motivated by the continuous growth of data sets and the rising complexity of data analysis tasks. To facilitate the analysis of big data, several parallel data processing frameworks, such as MapReduce and parallel data flow processors, have emerged. However, the implementation and tuning of parallel data analysis tasks requires expert knowledge and is very time-consuming and costly. Higher-level abstraction frameworks have been designed to ease the definition of analysis tasks. Optimizers can automatically generate efficient parallel execution plans from higher-level task definitions. Therefore, optimization is a crucial technology for massively parallel data analysis. This chapter presents the state of the art in optimization of parallel data flows. It covers higher-level languages for MapReduce, approaches to optimize plain MapReduce jobs, and optimization for parallel data flow systems. The optimization capabilities of those approaches are discussed and compared with each other. The chapter concludes with directions for future research on parallel data flow optimization.

A variety of techniques and tools exist to parallelize software systems on different parallel architectures (SIMD, MIMD). With the advances in high-speed networks, there has been a dramatic increase in the number of client/server applications. A variety of client/server applications are deployed today, ranging from simple telnet sessions to complex electronic commerce transactions. Industry standard protocols, like Secure Socket Layer (SSL), Secure Electronic Transaction (SET), etc., are in use for ensuring privacy and integrity of data, as well as for authenticating the sender and the receiver during message passing. Consequently, a majority of applications using parallel processing techniques are becoming synchronization-centric, i.e., for every message transfer, the sender and receiver must synchronize. However, more effective techniques and tools are needed to automate the clustering of such synchronization-centric applications to extract parallelism. The authors present a new clustering algorithm to facilitate the parallelization of software systems in a multiprocessor environment. The new clustering algorithm achieves traditional clustering objectives (reduction in parallel execution time, communication cost, etc.). Additionally, our approach: 1) reduces the performance degradation caused by synchronizations, and 2) avoids deadlocks during clustering. The effectiveness of our approach is depicted with the help of simulation results.

Many advanced ground-based solar telescopes improve the spatial resolution of observation images using an adaptive optics (AO) system. As any AO correction remains only partial, it is necessary to use post-processing image reconstruction techniques such as speckle masking or shift-and-add (SAA) to reconstruct a high-spatial-resolution image from atmospherically degraded solar images. In the New Vacuum Solar Telescope (NVST), the spatial resolution in solar images is improved by frame selection and SAA. In order to overcome the burden of massive speckle data processing, we investigate the possibility of using the speckle reconstruction program in a real-time application at the telescope site. The code has been written in the C programming language and optimized for parallel processing in a multi-processor environment. We analyze the scalability of the code to identify possible bottlenecks, and we conclude that the presented code is capable of being run in real-time reconstruction applications at NVST and future large aperture solar telescopes if care is taken that the multi-processor environment has low latencies between the computation nodes.

Background:For patient centered counseling to take place in community pharmacies, patients should feel encouraged to share their perspectives, yet studies show that this rarely happens. The process of patient perspective sharing relies on the interactional details that unfold during an encounter i.e. how patients verbally and nonverbally are encouraged to share their perspective, which in turn is affected by patients’ and pharmacy staff members’ psychological processes in the situation, i.e. how they perceive and feel when acting. Therefore, employing complimentary methods that study both interactional and psychological processes could deepen the understanding of the dynamics governing patients’ perspective sharing in pharmacy encounters.Objective:The objective of this study is twofold: 1) a methodological consideration of the benefits of employing Conversation Analysis (CA) and Video-Stimulated Recall Interviews (VSRI) in parallel, 2) to use the methodological combination to understand patient perspective sharing in community pharmacy interactions.Method:A single case study of one pharmacy encounter to explore the objectives in-depth. This was done through video recording of pharmacy encounters and subsequent CA-analysis; VSRIs were conducted with the involved patient and pharmacy staff member and analyzed using a qualitative thematic approach.Results:By exploring detailed interactional and psychological processes in parallel, specific occurrences which might hinder patients’ perspective sharing were revealed. CA demonstrated that staff member’s listening activities restricted the patient’s perspective sharing. VSRIs with patient and staff member supported this result: the staff member had a narrow conception of what counted as suitable answers and did not consider listening an active process. The patient harbored shame about needing to take the medication which affected her behavior during the encounter.Conclusion:The novelty of the methodological combination is promising in order to grasp the complex process of patient perspective sharing in pharmacy encounters, as it affords aspects such as emotionality to be considered a central part of pharmacy encounters. As a consequence, it is suggested that the psychological concept of mentalizing is added to pharmacy education, as it is a trainable capacity enabling staff to become aware of the mental states that affect both patients and staff themselves during the pharmacy encounter.

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore the paper summarises the Residual Network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16.