an evaluation model and benchmark for parallel computing frameworks
Clicks: 110
ID: 255779
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
110 views
7 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #48 of 83 articles by views in ui sahak
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
MARS and Spark are two popular parallel computing frameworks and widely used for large-scale data analysis. In this paper, we first propose a performance evaluation model based on support vector machine (SVM), which is used to analyze the performance of parallel computing frameworks. Furthermore, we give representative results of a set of analysis with the proposed analytical performance model and then perform a comparative evaluation of MARS and Spark by using representative workloads and considering factors, such as performance and scalability. The experiments show that our evaluation model has higher accuracy than multifactor line regression (MLR) in predicting execution time, and it also provides a resource consumption requirement. Finally, we study benchmark experiments between MARS and Spark. MARS has better performance than Spark in both throughput and speedup in the executions of logistic regression and Bayesian classification because MARS has a large number of GPU threads that can handle higher parallelism. It also shows that Spark has lower latency than MARS in the execution of the four benchmarks.
| Reference Key |
fan2018mobilean
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Weibei Fan;Zhijie Han;Ruchuan Wang |
| Journal | ui sahak |
| Year | 2018 |
| DOI |
10.1155/2018/3890341
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.