scholarly journals Tiny GPU Cluster for Big Spatial Data: A Preliminary Performance Evaluation

2015 ◽  
Author(s):  
Jianting Zhang ◽  
Simin You ◽  
Le Gruenwald
Keyword(s):  
Performance Evaluation ◽  
Spatial Data ◽  
Gpu Cluster

Performance Evaluation of a Human Immune System Simulator on a GPU Cluster

2015 ◽  
pp. 458-468 ◽  
Author(s):  
Thiago M. Soares ◽  
Micael P. Xavier ◽  
Alexandre B. Pigozzo ◽  
Ricardo Silva Campos ◽  
Rodrigo W. dos Santos ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Immune System ◽  
Human Immune System ◽  
Gpu Cluster ◽  
Human Immune

Distributed Multi-Source Spatial Data Fusion Model Construction and Performance Evaluation

2011 ◽  
Vol 460-461 ◽  
pp. 404-408
Author(s):  
Yue Shun He ◽  
Jun Zhang ◽  
Jie He
Keyword(s):  
Performance Evaluation ◽  
Data Fusion ◽  
Information Fusion ◽  
Spatial Data ◽  
Evaluation Model ◽  
Distributed Model ◽  
Fusion System ◽  
Fusion Model ◽  
Test Environment ◽  
Sensor Information

This paper mainly analyzed the principle of multi-source spatial data fusion, and expounded the multi-source spatial data fusion of the distributed model structure. The paper considers a distributed multi-sensor information fusion system factors, A performance evaluation model was established which was suitable for distributed multi-sensor information fusion system, It can estimate the system's precision, track quality, filtering quality, and the relevant between Navigation Paths and so on. Meanwhile, we had a lot of experiments by the datum which generated by the simulation test environment, experiments show that this evaluation model is valid.

Implementation and performance evaluation of a communication-avoiding GMRES method for stencil-based code on GPU cluster

2019 ◽  
Vol 75 (12) ◽  
pp. 8115-8146 ◽  
Author(s):  
Kazuya Matsumoto ◽  
Yasuhiro Idomura ◽  
Takuya Ina ◽  
Akie Mayumi ◽  
Susumu Yamada
Keyword(s):  
Performance Evaluation ◽  
Gmres Method ◽  
Gpu Cluster ◽  
And Performance

scholarly journals PREDICTION BASED WORKLOAD PERFORMANCE EVALUATION FOR DISASTER MANAGEMENT SPATIAL DATABASE

2018 ◽  
Vol XLII-4/W10 ◽  
pp. 187-192
Author(s):  
N. Suryana ◽  
M. S. Rohman ◽  
F. S. Utomo
Keyword(s):  
Performance Evaluation ◽  
Disaster Management ◽  
Spatial Data ◽  
Learning Algorithm ◽  
Spatial Database ◽  
Confusion Matrix ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Search Technique ◽  
Accuracy Measurement

<p><strong>Abstract.</strong> This paper discusses a prediction based workload performance evaluation implementation during Disaster Management, especially at the response phase, to handle large spatial data in the event of an eruption of the Merapi volcano in Indonesia. Complexity associated with a large spatial database are not the same with the conventional database. This implies that in coming complex work loads are difficult to be handled by human from which needs longer processing time and may lead to failure and undernourishment. Based on incoming workload, this study is intended to predict the associated workload into OLTP and DSS workload performance types. From the SQL statements, it is clear that the DBMS can obtain and record the process, measure the analysed performances and the workload classifier in the form of DBMS snapshots. The Case-Based Reasoning (CBR) optimised with Hash Search Technique has been adopted in this study to evaluate and predict the workload performance of PostgreSQL. It has been proven that the proposed CBR using Hash Search technique has resulted in acceptable prediction of the accuracy measurement than other machine learning algorithm like Neural Network and Support Vector Machine. Besides, the results of the evaluation using confusion matrix has resulted in very good accuracy as well as improvement in execution time. Additionally, the results of the study indicated that the prediction model for workload performance evaluation using CBR which is optimised by Hash Search technique for determining workload data on shortest path analysis via the employment of Dijkstra algorithm. It could be useful for the prediction of the incoming workload based on the status of the predetermined DBMS parameters. In this way, information is delivered to DBMS hence ensuring incoming workload information that is very crucial to determine the smooth works of PostgreSQL.</p>

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