PERFORMANCE PREDICTION VIA MODELING: A CASE STUDY OF THE ORNL CRAY XT4 UPGRADE

2009 ◽  
Vol 19 (04) ◽  
pp. 619-639 ◽  
Author(s):  
KEI DAVIS ◽  
KEVIN J. BARKER ◽  
DARREN J. KERBYSON
Keyword(s):  
Performance Prediction ◽  
Predictive Performance ◽  
High Accuracy ◽  
Performance Degradation ◽  
Network Resources ◽  
Performance Models ◽  
Actual Performance ◽  
Allocation Strategies ◽  
Node Allocation

We present predictive performance models of two of the petascale applications, S3D and GTC, from the DOE Office of Science workload. We outline the development of these models and demonstrate their validation on an Opteron/Infiniband cluster and the pre-upgrade ORNL Jaguar system (Cray XT3/XT4). Given the high accuracy of the full application models, we predict the performance of the Jaguar system after the upgrade of its nodes, and subsequently compare this to the actual performance of the upgraded system. We then analyze the performance of the system based on the models to quantify bottlenecks and potential optimizations. Finally, the models are used to quantify the benefits of alternative node allocation strategies, and to quantify performance degradation resulting from inter-process competition for network resources.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

Structural Identification for Performance Prediction Considering Uncertainties: Case Study of a Movable Bridge

2013 ◽  
Vol 139 (10) ◽  
pp. 1703-1715 ◽  
Author(s):  
Hasan Burak Gokce ◽  
F. Necati Catbas ◽  
Mustafa Gul ◽  
Dan M. Frangopol
Keyword(s):  
Performance Prediction ◽  
Structural Identification ◽  
Movable Bridge

Comparison of Wind Turbine Performance Prediction and Measurement

1982 ◽  
Vol 104 (2) ◽  
pp. 84-88 ◽  
Author(s):  
J. L. Tangler
Keyword(s):  
Test Data ◽  
Wind Turbines ◽  
Performance Prediction ◽  
State Of The Art ◽  
Correlation Study ◽  
Performance Models ◽  
Rocky Flats ◽  
Turbine Performance ◽  
Horizontal Axis Wind Turbines ◽  
Development Center

The purpose of this work was to evaluate the state-of-the-art of performance prediction for small horizontal-axis wind turbines. This effort was undertaken since few of the existing performance methods used to predict rotor power output have been validated with reliable test data. The program involved evaluating several existing performance models from four contractors by comparing their predictions for two wind turbines with actual test data. Test data were acquired by Rocky Flats Test and Development Center and furnished to the contractors after submission of their prediction reports. The results of the correlation study will help identify areas in which existing rotor performance models are inadequate and, where possible, the reasons for the models shortcomings. In addition, several problems associated with obtaining accurate test data will be discussed.

scholarly journals A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST

2021 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Kenta Shirane ◽  
Takahiro Yamamoto ◽  
Hiroyuki Tomiyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Critical Path ◽  
High Accuracy ◽  
Path Delay ◽  
Trade Off ◽  
Critical Path Delay

In this paper, we present a case study on approximate multipliers for MNIST Convolutional Neural Network (CNN). We apply approximate multipliers with different bit-width to the convolution layer in MNIST CNN, evaluate the accuracy of MNIST classification, and analyze the trade-off between approximate multiplier’s area, critical path delay and the accuracy. Based on the results of the evaluation and analysis, we propose a design methodology for approximate multipliers. The approximate multipliers consist of some partial products, which are carefully selected according to the CNN input. With this methodology, we further reduce the area and the delay of the multipliers with keeping high accuracy of the MNIST classification.

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