scholarly journals Visual programming of MPI applications: Debugging, performance analysis, and performance prediction

2014 ◽  
Vol 11 (4) ◽  
pp. 1315-1336 ◽  
Author(s):  
Stanislav Böhm ◽  
Marek Bĕhálek ◽  
Ondřej Meca ◽  
Martin Surkovský
Keyword(s):  
Performance Prediction ◽  
Memory Systems ◽  
Visual Programming ◽  
Parallel Applications ◽  
Colored Petri Nets ◽  
Visual Model ◽  
Performance Predictions ◽  
Mpi Applications ◽  
And Performance ◽  
Short Time

In our research, we try to simplify the development of parallel applications in the area of the scientific and engineering computations for distributed memory systems. The difficulties of this task lie not only in programming itself, but also in a complexity of supportive activities like debugging and performance analyses. We are developing a unifying framework where it is possible to create parallel applications and perform various supportive activities. The unifying element, that interconnects all these activities, is our visual model that is inspired by Colored Petri Nets. It is used to define the parallel behavior and the same model is used to show the inner state of the developed application back to the user. This paper presents how to extend this approach for debugging, tracing, and performance predictions. It also presents benefits obtained by their interconnection. The presented ideas are integrated into our open source tool Kaira (http://verif.cs.vsb.cz/kaira). Kaira is a prototyping tool, where a user can implement his/her ideas and experiment with them in a short time, create a real running program and verify its performance and scalability.

scholarly journals Performance Prediction of Cross-Flow Fans Using Mean Streamline Analysis

2005 ◽  
Vol 2005 (2) ◽  
pp. 112-116 ◽  
Author(s):  
Jae-Won Kim ◽  
Eun Young Ahn ◽  
Hyoung Woo Oh
Keyword(s):  
Performance Prediction ◽  
Cross Flow ◽  
Prediction Method ◽  
General Purpose ◽  
Operating Conditions ◽  
Performance Predictions ◽  
Performance Curves ◽  
Mean Streamline Analysis ◽  
And Performance ◽  
Cross Flow Fan

This paper presents the mean streamline analysis using the empirical loss correlations for performance prediction of cross-flow fans. Comparison of overall performance predictions with test data of a cross-flow fan system with a simplified vortex wall scroll casing and with the published experimental characteristics for a cross-flow fan has been carried out to demonstrate the accuracy of the proposed method. Predicted performance curves by the present mean streamline analysis agree well with experimental data for two different cross-flow fans over the normal operating conditions. The prediction method presented herein can be used efficiently as a tool for the preliminary design and performance analysis of general-purpose cross-flow fans.

Performance Prediction Method for Multihull Yachts

1989 ◽  
Author(s):  
Clay Oliver
Keyword(s):  
Performance Prediction ◽  
State Of The Art ◽  
Prediction Method ◽  
Aerodynamic Forces ◽  
Design Studies ◽  
Performance Predictions ◽  
Velocity Prediction ◽  
America’S Cup ◽  
And Performance ◽  
Scale Data

A new performance prediction method for multihull yachts is described. The methods described here, and performance predictions based on these methods were used in the design and modifications of the 1988 America's Cup Defender Stars & Stripes. The method incorporates the type of solution procedures used in state-of-the-art monohull velocity prediction programs. The various models used for hydrodynamic and aerodynamic forces are briefly discussed. The predictive method is validated using full-scale data from C-Class catamarans, a Formula 40 catamaran, a 75-foot “maxicat”, and Stars & Stripes with a soft-sail rig. Several examples of design studies are presented.

Communication Analysis and Performance Prediction of Parallel Applications on Large-Scale Machines

2016 ◽  
pp. 80-105 ◽  
Author(s):  
Yan Li ◽  
Jidong Zhai ◽  
Keqin Li
Keyword(s):  
Performance Prediction ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Parallel Applications ◽  
Network Simulator ◽  
Communication Performance ◽  
Key Factor ◽  
And Performance ◽  
High Performance Computers

With the development of high performance computers, communication performance is a key factor affecting the performance of HPC applications. Communication patterns can be obtained by analyzing communication traces. However, existing approaches to generating communication traces need to execute the entire parallel applications on full-scale systems that are time-consuming and expensive. Furthermore, for designers of large-scale parallel computers, it is greatly desired that performance of a parallel application can be predicted at the design phase. Despite previous efforts, it remains an open problem to estimate sequential computation time in each process accurately and efficiently for large-scale parallel applications on non-existing target machines. In this chapter, we will introduce a novel technique for performing fast communication trace collection for large-scale parallel applications and an automatic performance prediction framework with a trace-driven network simulator.

Synthetic Oil Is Better for Whom?

2010 ◽  
Vol 3 (3) ◽  
pp. 363-365 ◽  
Author(s):  
Lewis Hollweg
Keyword(s):  
Interest Groups ◽  
Mechanical Behavior ◽  
Performance Prediction ◽  
Organizational Psychology ◽  
Creative Destruction ◽  
Disruptive Technology ◽  
Negative Outcomes ◽  
Synthetic Oil ◽  
Performance Predictions ◽  
And Performance

After reviewing the article by Johnson et al. (2010), I began to ask myself and other industrial and organizational (I-O) psychologists what interest groups might be impacted by a Society for Industrial and Organizational Psychology (SIOP)-sponsored synthetic validity database and its resulting mechanical behavior and performance predictions. Who are the various stakeholders and what might be the positive or negative outcomes caused by this “disruptive technology” that could cause “creative destruction” in the I-O psychology profession? Among the consumers and producers of performance prediction, who might gain and who might be creatively destroyed? From these questions and the subsequent conversations, I identified the following categories and possibilities, but I am sure there are others that I have not anticipated.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

Experimental and computer aided solubility quantification of diverse lignins and performance prediction

2021 ◽  
Vol 57 (14) ◽  
pp. 1782-1785
Author(s):  
Olumoye Ajao ◽  
Marzouk Benali ◽  
Naïma El Mehdi
Keyword(s):  
Performance Prediction ◽  
Solvent Fractionation ◽  
Computer Aided ◽  
And Performance

New insights on the variability of solubility elucidated for diverse lignins, quantification thereby makes it possible to predict performance for solvent fractionation processes and polymers formulation.

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