Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Parallel software tools at Langley Research Center

This document gives a brief overview of parallel software tools available on the Intel iPSC/860 parallel computer at Langley Research Center. It is intended to provide a source of information that is somewhat more concise than vendor-supplied material on the purpose and use of various tools. Each of the chapters on tools is organized in a similar manner covering an overview of the functionality, access information, how to effectively use the tool, observations about the tool and how it compares to similar software, known problems or shortfalls with the software, and reference documentation. It is primarily intended for users of the iPSC/860 at Langley Research Center and is appropriate for both the experienced and novice user

Dynamic tuning of parallel programs

Performance of parallel programs is one of the reasons of their development. The process of designing and programming a parallel application is a very hard task that requires the necessary knowledge for the detection of performance bottlenecks, and the corresponding changes in the source code of the application to eliminate those bottlenecks. Current approaches to this analysis require a certain level of expertise from the developers part in locating and understanding the performance details of the application execution. For these reasons, we present an automatic performance analysis tool with the objective of alleviating the developers of this hard task: Kappa Pi. The most important limitation of KappaPi approach is the important amount of gathered information needed for the analysis. For this reason, we present a dynamic tuning system that takes measures of the execution on-line. This new design is focused to improve the performance of parallel programs during runtime.I Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

FADI: a fault-tolerant environment for open distributed computing

FADI is a complete programming environment that serves the reliable execution of distributed application programs. FADI encompasses all aspects of modern fault-tolerant distributed computing. The built-in user-transparent error detection mechanism covers processor node crashes and hardware transient failures. The mechanism also integrates user-assisted error checks into the system failure model. The nucleus non-blocking checkpointing mechanism combined with a novel selective message logging technique delivers an efficient, low-overhead backup and recovery mechanism for distributed processes. FADI also provides means for remote automatic process allocation on the distributed system nodes

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

[EN] This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.García Moreno, E.; Ponluisa, N.; Quiles Cucarella, E.; Zotovic Stanisic, R.; Gutiérrez, SC. (2022). Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors. Sensors. 22(1):1-29. https://doi.org/10.3390/s2201033212922