Parallel processing for real-time simulation: a case study

This paper provides a tutorial on the motivations, design, and applications of parallel processing applied to video real-time, illustrated by the experience gained in the implementation of the P/sup 3/I machine. Its main purpose is to highlight the motivations for such a development the basic implementation choices, the major difficulties encountered and how they have been solved. Through these studies we found that parallel processing is well-suited to video real-time, when programmable implementations are considered. There are many outcomes of the P/sup 3/I project, ranging from architectural considerations to parallel algorithms optimizations, and programming methodology. We want to emphasize three conclusions. First, programming an architecture composed of different parallel paradigms in a given architecture is tractable, and this heterogeneity is cost effective and efficient in terms of processing performances. Second, concerning the well known debate about how to match parallel architectures and image processing "levels" we conclude that the key is not to discuss Flynn's taxonomy (i.e., data versus tasks parallelism) but to consider how the parallelism grain evolves within a whole application. Third, we confirm that in the field of image processing, the efficiency of parallelism can only be gained if algorithms developers think "parallel"; this result seems to be obvious, but just consider the trends of recent RISC processors, embedding more and more parallelism, and claiming at a compatibility with existing sequential softwares.

This paper demonstrates Medusa, a programming framework for parallel graph processing on graphics processors (GPUs). Medusa enables developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime system of Medusa automatically executes the user-defined APIs in parallel on the GPU, with a series of graph-centric optimizations based on the architecture features of GPUs. We will demonstrate the steps of developing GPU-based graph processing algorithms with Medusa, and the superior performance of Medusa with both real-world and synthetic datasets.

Hypotheses H1: model-based constructability based on a parallel construction model should identify more constructability issues than traditional model-based constructability processes. H2: model-based quantity takeoff should take less time than manual take-off. H3: integrated location-based scheduling should enable schedule optimization over CPM–based approaches, resulting in shorter overall duration with more continuous resource use. Purpose: The goal of this paper is to present the case study of Kaiser Oakland hospital project, the largest known implementation of integrated 5D systems combined with location-based planning. Research Design/Method: Parallel processes were implemented in the project. The results of integrated 5D system were compared to the results of traditional constructability, estimating and scheduling processes. Findings: H1 and H3 were supported in the preconstruction of foundations. Over 200 additional constructability issues were identified using the construction model. Location-based scheduling enabled the planning of continuous work for subcontractors while compressing the duration of Foundation phase by six weeks. Contrary to the hypothesis H2 there was no time saving benefits related to quantity take-off, although there were some qualitative benefits in terms of better communication. Limitations: Only Foundation phase was analyzed. Parallel processes are a form of waste. Implications: Integrated 5D system was shown to provide better results for constructability and scheduling. Value for practitioners: Practitioners can learn from the challenges discussed in the paper. Use of an integrated 5D system has not been described in technical literature before using a case study of this size. Keywords: Location-based management, constructability, 5D model, flowline schedule Paper type: Case Study

An improved multi-channel graph convolutional network and its applications for rotating machinery diagnosis

This chapter describes parallel processing of robot-arm control computation and simulation. The parallel processing of robot control computation has attracted much attention to develop a cost-effective, compact, and advanced robot controller which allows a robot system to perform very complicated operations quickly and accurately even in hazardous environments, such as space and ocean floor. Also, parallel processing of robot motion simulation is important to efficiently develop the advanced robot hardwares and the controllers. This chapter introduces several parallel processing schemes on multiprocessor systems for the robot-arm control computation and the robot-arm simulation. More concretely, first of all, parallel processing schemes for the Newton-Euler equations for dynamic control are introduced. Secondly, how to implement the schemes on an actual multiprocessor system is discussed. Thirdly, parallel processing schemes for Walker & Orin’s algorithm for dynamics simulation and an implementation of the schemes on a multiprocessor system are described. Finally, future directions of the parallel processing of the robot control and simulation are briefly discussed.

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and deallocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

Evacuation simulation has the potential to be used as part of a decision support system during large-scale incidents to provide advice to incident commanders. To be viable in these applications, it is essential that the simulation can run many times faster than real time. Parallel processing is a method of reducing run times for very large computational simulations by distributing the workload amongst a number of processors. This paper presents the development of a parallel version of the rule based evacuation simulation software buildingEXODUS using domain decomposition. Four Case Studies (CS) were tested using a cluster, consisting of 10 Intel Core 2 Duo (dual core) 3.16 GHz CPUs. CS-1 involved an idealised large geometry, with 20 exits, intended to illustrate the peak computational speed up performance of the parallel implementation, the population consisted of 100,000 agents; the peak computational speedup (PCS) was 14.6 and the peak real-time speedup (PRTS) was 4.0. CS-2 was a long area with a single exit area with a population of 100,000 agents; the PCS was 13.2 and the PRTS was 17.2. CS-3 was a 50 storey high rise building with a population of 8000/16,000 agents; the PCS was 2.48/4.49 and the PRTS was 17.9/12.9. CS-4 is a large realistic urban area with 60,000/120,000 agents; the PCS was 5.3/6.89 and the PRTS was 5.31/3.0. This type of computational performance opens evacuation simulation to a range of new innovative application areas such as real-time incident support, dynamic signage in smart buildings and virtual training environments.

A parallel processing framework for the investigation of three dimensional subsurface geological problems is developed. The Floating Point Systems T-20 hypercube parallel computer system is used in the automatic correlation of petrophysical well logs. Parallel processing issues such as processor management, interprocessor communications management, memory management and algorithm development are discussed. Techniques are discussed to use spontaneous potential petrophysical logs to segregate subsurface strata into sand and shale segments. These segments are then used to generate sand/shale ratio logs for use in an automatic subsurface stratigraphic analysis system on a parallel machine. Phase one prepares petrophysical logs for stratigraphic analysis. The petrophysical logs are filtered and cleaned to remove any as salinity effects and improper tool responses. The spontaneous potential log is used to segregate each log into individual sand and shale beds and use these beds to create sand and shale databases which are used to create sand/shale ratio logs for later analysis. Phase two creates and solves a two dimensional matching probability matrix. The theoretical foundation is developed and several implementation issues are discussed. These issues include: (1) how to select data points, (2) how to compare data values from two petrophysical logs, (3) how to view the data, and (4) how to find the optimal set of strata observing several constraints imposed by the nature of the subsurface. Phase three developments parallel processing techniques to handle a multi-dimensional matching probability matrix. Each petrophysical log is treated as an axis of a multi-dimensional matrix with all points from one log being compared with all points from all other logs. A theoretical foundation is developed followed by implementation details. The data storage space required for this study was in excess of 10$\sp{56}$ bytes.

The computation of the highly coupled dynamic equations has always posed a bottleneck in real-time dynamic control of robot manipulators. Recent advances in VLSI technology make it possible to implement new algorithms that compute these equations and meet real-time constraints. Parallel processing techniques can now be used to reduce the computation time for models of a highly mathematical nature such as the dynamical modelling of robot manipulators. In this paper an attempt is made to review the subject of the parallel computation of robot dynamics. Moreover, a new and highly efficient technique is introduced to solve this problem. A simplified form of the Lagrange-Euler is divided into subtasks and distributed on to a parallel processing system. The development (parallel processor) system employs several INMOS transputers running the OCCAM concurrent programming language. Further, the system is used to introduce parallelism to robot dynamics through different task allocation strategies. These strategies flow naturally from the Lagrange-Euler formulation. The cost effectiveness and speed of the algorithm are demonstrated by a case study (Stanford arm). Comparisons are made between uniprocessor (von Neumann) and parallel implementations of the algorithm. Several measures such as utilisation, efficiency, and speed up are used to evaluate the performance of the employed networks and task allocations.

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

Sharing memory spaces between parallel processes is a common practice in a real-time simulation environment. In an environment where parallel processes can exist on the same machine or on different machines, it is a challenge to develop and maintain reusable software that interfaces with shared memory spaces. The two main problems involved in dealing with shared memory spaces are: 1) providing platform independent access to the memory space, and 2) providing to all processes a consistent view of the structure and content of the memory space. Object-oriented techniques were used to create a software architecture designed to manage shared memory spaces. Object-oriented design patterns were used to present client code with a conceptual model of shared memory spaces while concealing the underlying implementation details. The resulting architecture is largely computing platform independent, with platform specific code being isolated to a few selected classes. The design was implemented in C++ for the NASA Langley Standard Real-Time Simulation (LaSRS++) Application Framework. This paper provides an overview of the design and implementation of the LaSRS++ shared memory management software.

In this demonstration we will showcase CASPER, a novel tool that enables sequential data-intensive programs to automatically leverage the optimizations provided by parallel data processing frameworks. The goal of CASPER is to reduce the inertia against adaptation of new data processing frameworks---particularly for non-expert users---by automatically re-writing sequential programs written in general purpose languages to the high-level DSLs or APIs of these frameworks. Through CASPER's browser-based interface, users can enter the source code of their Java applications and have it automatically retargeted to execute on Apache Spark. In our interactive presentation, we will use CASPER to optimize sequential implementations of data visualization programs as well as image processing kernels. The optimized Spark implementations along with the original sequential implementations will then be executed simultaneously on the cloud to allow the demo to audience compare the runtime performances and outputs in real-time.

Real time fingerprint identification is usually equipped with specific computation machine architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint identification on common computation machine, a disquisition was conducted on sequential access method for fingerprint identification, with its underlying data structure designed to work without and with parallel processing. Hypothetically, parallel processing based on multi cores processor technology, can give faster result without reducing accuracy. Experiment confirms that speed performance of fingerprint identification using sequential access method with parallel processing outperforms the one without parallel processing. For both strategy, even though using parallel processing confirms faster result, experiment shows that searching time O(n) still linearly depends on number of fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access method utilization.

There is broad consensus that the therapeutic relationship constitutes a core common factor for almost all modalities of psychotherapy and viewed as necessary and sufficient in person-centred therapy serving as a catalyst for change. However, ruptures can occur in the therapeutic relationship and at an extreme end can result in complaints, some of which may be deemed frivolous. The potential for frivolous complaints can arise for any therapist, regardless of their competence or integrity, and may come from clients, colleagues, or third parties. Frivolous complaints are defined as groundless grievances, made without merit with the intention to cause distress, damage or harassment for the recipient. Little qualitative research has been undertaken to understand the personal and professional impact of a frivolous complaint. This study was two-phased. A qualitative design was employed. Online semi-structured interviews were conducted in both phases to explore first-person lived experiences of the phenomenon. The findings were analysed from an interpretative phenomenological perspective. The aim of phase (1) was to understand the personal and professional consequences of frivolous complaints on a group of person-centred psychotherapists. The aim of phase (2) was to understand the lived experiences of a supervisor who supported a supervisee through the complaints process. The specific objectives were to highlight participants’ sense-making of receiving a frivolous complaint in the context of their personal and professional lives; how participants managed the complaint and complaints process; a supervisor’s lived experience of supporting a supervisee through the complaints process; the role of the supervisor in the complaints process. Phase (1): eight person-centred psychotherapists including three males and five females from the United Kingdom or Ireland participated in this study. Participants were aged between 50-65, with 6+ years of psychotherapy experience. A case-by-case idiographic analysis was followed by a cross-case analysis. Phase (1) findings indicated the participants experienced an unfounded frivolous complaint as causing enduring psychological and professional harm. Three themes emerged: ‘Relationships,’ ‘Ruptures,’ and ‘Resolution.’ Receiving a frivolous complaint was akin to a traumatic event and challenged personal and professional identity. Participants felt unprepared, vulnerable, anxious, self-doubting, feared judgment and stigma, experienced professional isolation, were affected by a power imbalance due to the burden of proof, faced challenges in the complaints process due to an absence of complaints management training, lacked supportive intervention, and altered their professional practice. Phase (2): a single case study focussed on a male supervisor in the 51-65 age range practising as a person-centred psychotherapist for over 20 years. The interview generated a description of his experience around supporting a supervisee during the complaints process. Phase (2) findings - four themes emerged from an idiographic analysis of the case: ‘Them and Us – Shutters Down,’ ‘Containing the Container,’ ‘Reflecting and Reasoning,’ and ‘Parallel Processes.’ Results indicated that the clinical supervisor experienced isolation, power imbalance affecting their ability to advocate for the supervisee, was disappointed in the absence of complaints management training, and lack of containment. They believed client factors impacted complaints, and their feelings mirrored that of the supervisees. Previous research tended to focus on the effects of ethical violations on mental health professionals or the complainant. The findings of this study fill a gap in knowledge and make a positive contribution to knowledge on a theoretical level by focussing specifically on the consequences of frivolous complaints in a psychotherapy context. On a practical level the results can be used to review guidance on personal and professional support, education and awareness, training and systemic improvements by streamlining investigatory complaint procedures across all accrediting bodies. It may also help foster a culture of empathy and accountability in order to re-establish emotional well-being after a complaint is made. This study builds on previous research by proposing a new framework to improve containment by providing a structured, supportive space to process emotions and foster professional growth for affected supervisors and their supervisees. The phenomenological perspective contributes to existing literature by offering new insights into the lived experience of person-centred therapists and the impact of frivolous complaints.

Real-time data integration from sources becomes critical in today's world to make efficient decisions for industries across finance, health, and retail. Informatica PowerCenter is considered one of the fastest and most fully featured Transform, Load (ETL) tools that face many challenges in real-time cloud integrations through Snowflake and Microsoft Azure, among many more. This assignment looks at a study of how real-time cloud integration is enabled by Informatica PowerCenter, as well as its architecture, features, and applicability [8]. With the key features of Change Data Capture (CDC), parallel processing, and workflow automation, PowerCenter manages high-frequency data ingestion with minimum latency and data consistency. We discuss some of the critical challenges around latency, security, and cost management and review the functionality of PowerCenter in the cloud, which is a scalable platform that allows compliance with industry standards. These findings show that with the integration of cloud platforms, PowerCenter can take the real-time processing of data further, thereby providing a scalable solution to organizations for pursuing analytics on the cloud. Many case studies in the financial and healthcare industries focus on how the PowerCenter toolset can drive operational efficiencies and timely decision-making. Therefore, this suggests that future advanced automation and machine learning integration would be recommended in this sphere of research at PowerCenter, optimizing real-time data integration.