scholarly journals A three-phase workflow for general and expressive representations of nondeterminism in HPC applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1175-1184
Author(s):  
Dylan Chapp ◽  
Danny Rorabaugh ◽  
Kento Sato ◽  
Dong H Ahn ◽  
Michela Taufer
Keyword(s):  
Machine Learning ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Structural Similarity ◽  
Machine Learning Techniques ◽  
Graph Kernels ◽  
Learning Techniques ◽  
Three Phase ◽  
Performance Computing

Nondeterminism is an increasingly entrenched property of high-performance computing (HPC) applications and has recently been shown to seriously hamper debugging and reproducibility efforts. Tools for addressing the nondeterministic debugging problem have emerged, but they do not provide methods for systematically cataloging the nondeterminism in a given application. We propose a three-phase workflow for representing executions of nondeterministic message passing interface programs as event graphs, quantifying their structural similarity with graph kernels, and applying machine learning techniques to investigate shared properties across applications. We present an empirical study comparing two graph kernels’ suitability for this task and propose future uses of the methodology.

scholarly journals An Investigation of Weather Forecasting using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 72-78
Author(s):  
Mrs. N.Vanitha ◽  
J.Haritha
Keyword(s):  
Machine Learning ◽  
Material Science ◽  
High Performance ◽  
Weather Forecasting ◽  
Machine Learning Techniques ◽  
Distant Future ◽  
Climate Conditions ◽  
Learning Techniques ◽  
Complex Models ◽  
Performance Computing

Customarily, climate expectations are performed with the assistance of enormous complex models of material science, which use distinctive air conditions throughout a significant stretch of time. In this paper, we studied  a climate expectation  strategy that uses recorded information from  numerous climate stations to prepare basic AI models, which can give usable figures about certain climate conditions for the not so distant future inside a brief  timeframe These conditions are frequently flimsy on account of annoyances of the climate framework, making the models give mistaken estimates.[1] The model are for the most part run on many hubs in an enormous High Performance Computing (HPC) climate which burns through a lot of energy.. The modes can be run on significantly less asset serious conditions. In this paper we describe that the sufficient to be utilized status of the workmanship methods. Moreover, we described that it is valuable to use the climate stations information from various adjoining territories over the information of just the region for which climate anticipating is being performed.

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

scholarly journals Provenance-and machine learning-based recommendation of parameter values in scientific workflows

2021 ◽  
Vol 7 ◽  
pp. e606
Author(s):  
Daniel Silva Junior ◽  
Esther Pacitti ◽  
Aline Paes ◽  
Daniel de Oliveira
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
User Preferences ◽  
Scientific Workflows ◽  
Machine Learning Techniques ◽  
Provenance Data ◽  
Learning Techniques ◽  
Parameter Values ◽  
And Storage ◽  
Composition Monitoring

Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturing, and storage of the data generated during execution. In some cases, they also provide components to ease the visualization and analysis of the generated data. During the workflow’s composition phase, programs must be selected to perform the activities defined in the workflow specification. These programs often require additional parameters that serve to adjust the program’s behavior according to the experiment’s goals. Consequently, workflows commonly have many parameters to be manually configured, encompassing even more than one hundred in many cases. Wrongly parameters’ values choosing can lead to crash workflows executions or provide undesired results. As the execution of data- and compute-intensive workflows is commonly performed in a high-performance computing environment e.g., (a cluster, a supercomputer, or a public cloud), an unsuccessful execution configures a waste of time and resources. In this article, we present FReeP—Feature Recommender from Preferences, a parameter value recommendation method that is designed to suggest values for workflow parameters, taking into account past user preferences. FReeP is based on Machine Learning techniques, particularly in Preference Learning. FReeP is composed of three algorithms, where two of them aim at recommending the value for one parameter at a time, and the third makes recommendations for n parameters at once. The experimental results obtained with provenance data from two broadly used workflows showed FReeP usefulness in the recommendation of values for one parameter. Furthermore, the results indicate the potential of FReeP to recommend values for n parameters in scientific workflows.

scholarly journals Machine Learning in Agriculture Application: Algorithms and Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 1140-1146
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Network Models ◽  
Processing Technique ◽  
Machine Learning Techniques ◽  
Image Processing Technique ◽  
Neural Network Models ◽  
Survey Paper ◽  
Learning Techniques

Machine learning techniques with high performance computing technologies can create various new opportunities in the agriculture domain. This paper does comprehensivereview of various papers which are concentrating on machine learning (ML) and deep learning application in agriculture. This paper is categorized into three sections a) Yield prediction using machine learning technique b) Price prediction c) Leaf disease detection using neural networks. In this paper we study the comparison of neural network models with existing models. The findings of this survey paper indicate Deep learning models give high accuracy and outperform traditional image processing technique and ML techniques outperforms various traditional techniques in prediction.

scholarly journals Model approach of a robotic manipulator 3DOF using Machine Learning

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Darielson Souza ◽  
Josias Batista ◽  
Laurinda Reis ◽  
Antonio De Souza Junior
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Computational Intelligence ◽  
Machine Learning Techniques ◽  
Robotic Arm ◽  
Learning Techniques ◽  
Three Phase ◽  
And Robotics ◽  
Model Approach

Applications of robotics have been steadily expanding in recent years, and robotics is evolving every day. Currently, robotics is seen as an important area in many applications. Robotics and computational intelligence are increasingly working in parallel with the goal of better performance and productivity. This work has the objective of making an modeling of a robotic arm with three phase induction motor through machine learning techniques to obtain a better model that represents the plant. The techniques used were Articial Neural Network (ANNs): MLP and ELM. The techniques obtained a good performance, and they were evaluated through the multi-correlation coecient for a comparative analysis.

scholarly journals Improved Failure Detection and Propagation Mechanisms for MPI

2021 ◽  
Author(s):  
Pedro Henrique Di Francia Rosso ◽  
Emilio Francesquini
Keyword(s):  
Fault Tolerance ◽  
High Performance Computing ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Failure Detection ◽  
Essential Components ◽  
Failure Propagation ◽  
Performance Computing

The Message Passing Interface (MPI) standard is largely used in High-Performance Computing (HPC) systems. Such systems employ a large number of computing nodes. Thus, Fault Tolerance (FT) is a concern since a large number of nodes leads to more frequent failures. Two essential components of FT are Failure Detection (FD) and Failure Propagation (FP). This paper proposes improvements to existing FD and FP mechanisms to provide more portability, scalability, and low overhead. Results show that the methods proposed can achieve better or at least similar results to existing methods while providing portability to any MPI standard-compliant distribution.

Exascale Message Passing Interface based Program Deadlock Detection

2016 ◽  
Vol 6 (2) ◽  
pp. 887
Author(s):  
Raed AlDhubhani ◽  
Fathy Eassa ◽  
Faisal Saeed
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Deadlock Detection ◽  
Efficient Manner ◽  
Parallel Processes ◽  
Critical Issues ◽  
Near Future ◽  
Performance Computing ◽  
Standard Library

Deadlock detection is one of the main issues of software testing in High Performance Computing (HPC) and also inexascale computing areas in the near future. Developing and testing programs for machines which have millions of cores is not an easy task. HPC program consists of thousands (or millions) of parallel processes which need to communicate with each other in the runtime. Message Passing Interface (MPI) is a standard library which provides this communication capability and it is frequently used in the HPC. Exascale programs are expected to be developed using MPI standard library. For parallel programs, deadlock is one of the expected problems. In this paper, we discuss the deadlock detection for exascale MPI-based programs where the scalability and efficiency are critical issues. The proposed method detects and flags the processes and communication operations which are potential to cause deadlocks in a scalable and efficient manner. MPI benchmark programs were used to test the proposed method.

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