Estimation of Job Execution Time in MapReduce on GPU clusters
碩士 === 國立成功大學 === 資訊工程學系 === 102 === With the development of GPU, there are hundreds of cores on one GPU which is much more than a four-core or eight-core CPU. It starts new direction of high speed computing. GPU computing’s performance is much better than before. Cloud computing also developed rapi...
Main Authors: | YangHung, 洪洋 |
---|---|
Other Authors: | Sheng-Tzong Cheng |
Format: | Others |
Language: | en_US |
Published: |
2014
|
Online Access: | http://ndltd.ncl.edu.tw/handle/75450627727195594798 |
Similar Items
-
Using Petri Net to Estimate Job Execution Time in MapReduce Model
by: Hsi-ChuanWang, et al.
Published: (2013) -
ATGMR: An Adaptive Task Granularity Scheme for GPU-CPU MapReduce Clusters
by: Chin-FaSu, et al.
Published: (2014) -
Adaptive MapReduce Framework for Multi-Application Processing on GPU
by: Hung-YuChang, et al.
Published: (2013) -
A Workload Balanced MapReduce Framework on GPU Platforms
by: Zhang, Yue
Published: (2015) -
Estimating runtime of a job in Hadoop MapReduce
by: Narges Peyravi, et al.
Published: (2020-07-01)