Toward Green Computing: Striking the Trade-Off between Memory Usage and Energy Consumption of Sequential Pattern Mining on GPU
Journal
1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018
Pages
152-155
Date Issued
2018
Author(s)
Abstract
The energy consumption of the data centers grows rapidly in the recent years, especially due to the increasing usage of the energy consuming GPU. Note that reducing the energy consumption can not only lower the operating cost but also increase the capacity of the data center. Therefore, it is important to explore the energy efficiency of an algorithm. In this work, we analyze a trade-off between the mining efficiency and the energy consumption of parallel sequential pattern mining on GPU. Sequential pattern mining is an important topic in the field of knowledge discovery since accelerating sequential pattern mining enables the possibility of various real-time recommendation systems and many other applications. The knowledge of the trade-off helps to decide the best hardware configuration for data centers as well as develop a task scheduling algorithm in a heterogeneous environment with better energy efficiency. We first study how the memory usage affects the execution time with a program profiler. Then, we apply GPU power models to find the relationship between the memory usage and the energy consumption. Finally, we conduct extensive experiments on both synthetic and real datasets to validate our results. © 2018 IEEE.
SDGs
Other Subjects
Computer hardware; Data mining; Economic and social effects; Energy efficiency; Energy utilization; Graphics processing unit; Knowledge engineering; Program processors; Real time systems; Scheduling algorithms; Data centers; Execution time; GPGPU; Hardware configurations; Heterogeneous environments; Real data sets; Sequential-pattern mining; Task-scheduling algorithms; Green computing
Type
conference paper