CounterMiner: Mining Big Performance Data from Hardware Counters
Yirong Lv, Bin Sun, Qingyi Luo, Jing Wang, Zhibin Yu, Xuehai Qian · 2018
Modern processors typically provide a small number of hardware performance counters to capture a large number of microarchitecture events. These counters can easily generate a huge amount (e.g., GB or TB per day) of data, which we call big performance data in cloud computing platforms with more than thousands of servers and millions of complex workloads running in a "24/7/365" manner. The big performance data provides a precious foundation for root cause analysis of performance bottlenecks, architecture and compiler optimization, and many more. However, it is challenging to extract value from the big performance data due to: 1) the many unperceivable errors (e.g., outliers and missing values); and 2) the difficulty of obtaining insights, e.g., relating events to performance. In this paper, we propose CounterMiner, a rigorous methodology that enables the measurement and understanding of big performance data by using data mining and machine learning techniques. It includes three novel components: 1) using data cleaning to improve data quality by replacing outliers and filling in missing values; 2) iteratively quantifying, ranking, and pruning events based on their importance with respect to performance; 3) quantifying interaction intensity between two events by residual variance. We use sixteen benchmarks (eight from CloudSuite and eight from the Spark version of HiBench) to evaluate CounterMiner. The experimental results show that CounterMiner reduces the average error from 28.3% to 7.7% when multiplexing 10 events on 4 hardware counters. We also conduct a real-world case study, showing that identifying important configuration parameters of Spark programs by event importance is much faster than directly ranking the importance of these parameters.