The results show that PETS outperforms a machine learning based method, and achieves speedups of up to x4.78 and convergence speed as low as 2 iterations.
Abstract—Spark tuning with its dozens of parameters for performance improvement is both a challenge and time con- suming effort. Current techniques rely on ...
PETS: Bottleneck-Aware Spark Tuning with Parameter Ensembles. TBG Perez, W Chen, R Ji, L Liu, X Zhou. 2018 27th International Conference on Computer ...
PETS: Bottleneck-Aware Spark Tuning with Parameter Ensembles. TBG Perez, W ... Leveraging Spark Performance with Bottleneck-Aware Tuning, Caching and Scheduling.
Apr 25, 2024 · Bottleneck-Aware Task Scheduling Based on Per-Stage and Multi ... PETS: Bottleneck-Aware Spark Tuning with Parameter Ensembles. ICCCN ...
This work presents a more elaborate solution in the form of self-adaptive executors which are able to continuously monitor the underlying system resources ...
The first, a tuning system called PETS, which stands for Param- eter Ensemble Table for Spark, was motivated from the chal- lenges and time consuming effort ...
PETS: Bottleneck-Aware Spark Tuning with Parameter Ensembles. ICCCN 2018: 1-9. [+][–]. Coauthor network. maximize. Note that this feature is a work in progress ...
In this work, we investigate the impact of the most important of the tunable Spark parameters on the application performance and guide developers on how to ...
Jan 15, 2020 · In this paper, we address the challenge of analyzing simulation data on HPC systems by using Apache Spark, which is a Big Data framework.