CHOPPER repartitions data as needed to ensure efficient task granularity, avoids data skew, and reduces shuffle traffic. Thus, CHOPPER allows users to write ...
Abstract—The performance of in-memory based data analytic frameworks such as Spark is significantly affected by how data is partitioned.
CHOPPER is a system for automatically determining the optimal number of partitions for each phase of a workload and dynamically changing the partition ...
In this study, it was proven that there is no correlation between the data size of the workload and the unrecoverable memory in order to shorten the training ...
CHOPPER: Optimizing data partitioning for in-memory data analytics frameworks. Arnab K. Paul; Wenjie Zhuang; et al. 2016; CLUSTER 2016. Focus areas ...
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CHOPPER: Optimizing Data Partitioning for In-Memory Data Analytics Frameworks. In Proc. IEEE Cluster 2016 (24%). Luna Xu, Min Li, Li Zhang, Ali R. Butt ...
Chopper: Optimizing data partitioning for in-memory data analytics frameworks. AK Paul, W Zhuang, L Xu, M Li, MM Rafique, AR Butt. 2016 IEEE International ...
Butt, “CHOPPER: Optimizing Data Partitioning for In-memory Data. Analytics Frameworks,” in 2016 IEEE International Conference on. Cluster Computing (CLUSTER) ...
Big Data 2017. CHOPPER: Optimizing data partitioning for in-memory data analytics frameworks. Arnab K. Paul; Wenjie Zhuang; et al. 2016; CLUSTER 2016. MEMTUNE ...
CHOPPER: Optimizing Data Partitioning for In-memory Data Analytics Frameworks pp. 110-119. Improving Collective I/O Performance Using Non-volatile Memory ...