scholarly journals Generic Platform for Failure Recovery in Survivable Trees

10.14311/780 ◽  
2005 ◽  
Vol 45 (6) ◽  
Author(s):  
V. Dynda
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Routing Algorithm ◽  
General Purpose ◽  
Failure Recovery ◽  
Dependable Systems ◽  
Tree Topologies ◽  
On Line ◽  
Recovery Mechanisms ◽  
Specific Fragment

Failure recovery is a fundamental task of the dependable systems needed to achieve fault-tolerant communications, smooth operation of system components and a comfortable user interface. Tree topologies are fragile, yet they are quite popular structures in computer systems. The term survivable tree denotes the capability of the tree network to deliver messages even in the presence of failures. In this paper, we analyze the characteristics of large-scale overlay survivable trees and identify the requirements for general-purpose failure recovery mechanisms in such an environment. We outline a generic failure recovery platform for preplanned tree restoration which meets those requirements, and we focus primarily on its completeness and correctness properties. The platform is based on bypass rings and it uses a bypass routing algorithm to ensure completeness, and specialized leader election to guarantee correctness. The platform supports multiple, on-line and on-the-fly recovery, provides an optional level of fault-tolerance, protection selectivity and optimization capability. It is independent of the the protected tree type (regarding traffic direction, number of sources, etc.) and forms a basis for application-specific fragment reconnection. 

scholarly journals Exploring Parallel MPI Fault Tolerance Mechanisms for Phylogenetic Inference with RAxML-NG

2021 ◽  
Author(s):  
Lukas Hübner ◽  
Alexey M. Kozlov ◽  
Demian Hespe ◽  
Peter Sanders ◽  
Alexandros Stamatakis
Keyword(s):  
Fault Tolerance ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Fault Tolerant ◽  
Phylogenetic Inference ◽  
Molecular Data ◽  
Supplementary Information ◽  
Tolerance Mechanisms ◽  
Recovery Mechanisms ◽  
Mpi Implementation

Phylogenetic trees are now routinely inferred on large scale HPC systems with thousands of cores as the parallel scalability of phylogenetic inference tools has improved over the past years to cope with the molecular data avalanche. Thus, the parallel fault tolerance of phylogenetic inference tools has become a relevant challenge. To this end, we explore parallel fault tolerance mechanisms and algorithms, the software modifications required, and the performance penalties induced via enabling parallel fault tolerance by example of RAxML-NG, the successor of the widely used RAxML tool for maximum likelihood based phylogenetic tree inference. We find that the slowdown induced by the necessary additional recovery mechanisms in RAxML-NG is on average 2%. The overall slowdown by using these recovery mechanisms in conjunction with a fault tolerant MPI implementation amounts to 8% on average for large empirical datasets. Via failure simulations, we show that RAxML-NG can successfully recover from multiple simultaneous failures, subsequent failures, failures during recovery, and failures during checkpointing. Recoveries are automatic and transparent to the user. The modified fault tolerant RAxML-NG code is available under GNU GPL at https://github.com/lukashuebner/ft-raxml-ng Contact: lukas.huebner@{kit.edu,h-its.org};, [email protected], [email protected], [email protected], [email protected] Supplementary information: Supplementary data are available at bioRχiv.

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

Research on highly parallel embedded control system design and implementation method

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 44-46
Author(s):  
Masato Edahiro ◽  
Masaki Gondo
Keyword(s):  
Computer Architecture ◽  
Intelligent Systems ◽  
Large Scale ◽  
General Purpose ◽  
Heterogeneous Structure ◽  
Single Chip ◽  
Powertrain Control ◽  
Processing Power ◽  
Hardware Description ◽  
Many Core

The pace of technology's advancements is ever-increasing and intelligent systems, such as those found in robots and vehicles, have become larger and more complex. These intelligent systems have a heterogeneous structure, comprising a mixture of modules such as artificial intelligence (AI) and powertrain control modules that facilitate large-scale numerical calculation and real-time periodic processing functions. Information technology expert Professor Masato Edahiro, from the Graduate School of Informatics at the Nagoya University in Japan, explains that concurrent advances in semiconductor research have led to the miniaturisation of semiconductors, allowing a greater number of processors to be mounted on a single chip, increasing potential processing power. 'In addition to general-purpose processors such as CPUs, a mixture of multiple types of accelerators such as GPGPU and FPGA has evolved, producing a more complex and heterogeneous computer architecture,' he says. Edahiro and his partners have been working on the eMBP, a model-based parallelizer (MBP) that offers a mapping system as an efficient way of automatically generating parallel code for multi- and many-core systems. This ensures that once the hardware description is written, eMBP can bridge the gap between software and hardware to ensure that not only is an efficient ecosystem achieved for hardware vendors, but the need for different software vendors to adapt code for their particular platforms is also eliminated.

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

i-com ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 139-151
Author(s):  
Thomas Schmidt ◽  
Miriam Schlindwein ◽  
Katharina Lichtner ◽  
Christian Wolff
Keyword(s):  
Emotion Recognition ◽  
Regression Models ◽  
Affective Computing ◽  
Large Scale ◽  
General Purpose ◽  
Thinking Aloud ◽  
Continue Research ◽  
Usability Metrics ◽  
Recognition Software ◽  
The Relationship

AbstractDue to progress in affective computing, various forms of general purpose sentiment/emotion recognition software have become available. However, the application of such tools in usability engineering (UE) for measuring the emotional state of participants is rarely employed. We investigate if the application of sentiment/emotion recognition software is beneficial for gathering objective and intuitive data that can predict usability similar to traditional usability metrics. We present the results of a UE project examining this question for the three modalities text, speech and face. We perform a large scale usability test (N = 125) with a counterbalanced within-subject design with two websites of varying usability. We have identified a weak but significant correlation between text-based sentiment analysis on the text acquired via thinking aloud and SUS scores as well as a weak positive correlation between the proportion of neutrality in users’ voice and SUS scores. However, for the majority of the output of emotion recognition software, we could not find any significant results. Emotion metrics could not be used to successfully differentiate between two websites of varying usability. Regression models, either unimodal or multimodal could not predict usability metrics. We discuss reasons for these results and how to continue research with more sophisticated methods.

WWW.TELD.NET: Online Courseware Engine for Teaching by Examples and Learning by Doing

2001 ◽  
Vol 29 (3) ◽  
pp. 219-235 ◽  
Author(s):  
G. Q. Huang ◽  
B. Shen ◽  
K. L. Mak
Keyword(s):  
Teaching And Learning ◽  
Virtual Classroom ◽  
Learning By Doing ◽  
General Purpose ◽  
Project Based Learning ◽  
Course Materials ◽  
Summative Assessments ◽  
On Line ◽  
Set Up ◽  
Electronic Delivery

TELD stands for “Teaching by Examples and Learning by Doing.” It is an on-line courseware engine over the World Wide Web. There are four folds of meanings in TELD. First, TELD represents a teaching and learning method that unifies a number of contemporary methods such as Problem-Based Learning (PBL) in medical education, Project-Based Learning (PBL) in engineering education, and Case Method (CM) in business education. Second, TELD serves as a Web server for hosting teaching and learning materials especially based on the TELD method. A variety of on-line facilities are provided for editing and uploading course materials such as syllabus, schedule, curriculum, examples of case study, exercises of mini-project, formative and summative assessments, etc. Third, TELD is a courseware search engine where educators are able to register their course materials and search for materials suitable for a particular course. In contrast with general-purpose search engines, TELD is set up for the special purpose of education. Therefore, the time and efforts spent on surfing are expected to be reduced dramatically. Finally, TELD is an on-line virtual classroom for electronic delivery of electronic curriculum materials. In addition to providing the lecture notes, TELD not only provides discussion questions for conducting in-class discussions and homework as formative assessment but also provides facilities for students to plan and submit their group work. This article presents an overview of the TELD courseware engine together with its background and underlying philosophy.

An efficient fault-tolerant routing algorithm in NoCs to tolerate permanent faults

2016 ◽  
Vol 72 (12) ◽  
pp. 4629-4650 ◽  
Author(s):  
Reza Akbar ◽  
Ali Asghar Etedalpour ◽  
Farshad Safaei
Keyword(s):  
Fault Tolerant ◽  
Routing Algorithm ◽  
Permanent Faults
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