| Jia Zhen (贾禛),Zhan Jianfeng,Wang Lei,Zhang Lixin.[J].高技术通讯(英文),2017,23(3):245~251 |
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| Characterizing big data analytics workloads on POWER8 SMT processors |
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| DOI：10.3772/j.issn.1006-6748.2017.03.003 |
| 中文关键词: |
| 英文关键词: simultaneous multithreading (SMT), workloads characterization, POWER8, big data analytics |
| 基金项目: |
| Author Name | Affiliation | | Jia Zhen (贾禛) | | | Zhan Jianfeng | | | Wang Lei | | | Zhang Lixin | |
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| 中文摘要: |
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| 英文摘要: |
| Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence, it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT (simultaneous multithreading) processors, which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform, which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results, this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly. |
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