A framework for parallel simulation application performance evaluation and optimization

2011 ◽  
Author(s):  
Z. H. Zhang ◽  
T. Fei ◽  
X. D. Chai
Keyword(s):  
Performance Evaluation ◽  
Parallel Simulation ◽  
Application Performance

Research and Application of Efficient Operation Technology

2014 ◽  
Vol 1030-1032 ◽  
pp. 1760-1763
Author(s):  
Yan Xia Li ◽  
Zheng Long Shao ◽  
Nai Jia Liu ◽  
Yu Peng
Keyword(s):  
Performance Evaluation ◽  
High Efficiency ◽  
Low Cost ◽  
End Users ◽  
Application Performance ◽  
Efficient Operation ◽  
High Satisfaction ◽  
Virtualization Technology ◽  
Efficient Integration

With the development of information, the number of applications is growing. The questions of cost, support, risk and safety also arise. In order to realize low cost and high efficiency, we have studied the efficient operation technologies, including application isolation technology, virtualization technology, performance evaluation technology, etc. Using these technologies, we have realized efficient integration of operation resources, comprehensive optimization of application performance, and high satisfaction of end users.

scholarly journals Performance Evaluation of a Big Data Application on Apache Spark

2021 ◽  
Author(s):  
Jeanne Alcantara
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Execution Time ◽  
Apache Spark ◽  
Massive Data ◽  
Application Performance ◽  
Data Application ◽  
Size Number ◽  
Big Data Application ◽  
The Impact

Apache Spark enables a big data application—one that takes massive data as input and may produce massive data along its execution—to run in parallel on multiple nodes. Hence, for a big data application, performance is a vital issue. This project analyzes a WordCount application using Apache Spark, where the impact on the execution time and average utilization is assessed. To facilitate this assessment, the number of executor cores and the size of executor memory are varied across different sizes of data that the application has to process, and the different number of nodes in the cluster that the application runs on. It is concluded that different pairs (data size, number of nodes in the cluster) require different number of executor cores and different size of executor memory to obtain optimum results for execution time and average node utilization.

scholarly journals Performance Evaluation of a Big Data Application on Apache Spark

2021 ◽  
Author(s):  
Jeanne Alcantara
Keyword(s):  
Big Data ◽  
Performance Evaluation ◽  
Execution Time ◽  
Apache Spark ◽  
Massive Data ◽  
Application Performance ◽  
Data Application ◽  
Size Number ◽  
Big Data Application ◽  
The Impact

Apache Spark enables a big data application—one that takes massive data as input and may produce massive data along its execution—to run in parallel on multiple nodes. Hence, for a big data application, performance is a vital issue. This project analyzes a WordCount application using Apache Spark, where the impact on the execution time and average utilization is assessed. To facilitate this assessment, the number of executor cores and the size of executor memory are varied across different sizes of data that the application has to process, and the different number of nodes in the cluster that the application runs on. It is concluded that different pairs (data size, number of nodes in the cluster) require different number of executor cores and different size of executor memory to obtain optimum results for execution time and average node utilization.

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