IDDCA: A New Clustering Approach For Sampling

Clustering methods are machine-learning algorithms that can be used to easily select the most representative samples within a huge program trace. k-means is a popular clustering method for sampling. While k-means performs well, it has several shortcomings: (1) it depends on a random initialization, so that clustering results may vary across runs; (2) the maximal number of clusters is a user-selected parameter, but its optimal value can be benchmark/trace-dependent; (3) k-means is a multi-pass algorithm which may be less practical for a large number of intervals. To solve these issues, we adapted an alternative clustering method, called DCA, to the issue of sampling. Unlike k-means, DCA and its sampling-specific adaptation IDDCA do not require the user to be exposed to internal clustering parameters: it dynamically defines the number of clusters for each target program and the method parameters dynamically adapt to the target program. For an ordered input (e.g., a trace of intervals), the method is deterministic. Finally, it is an online and thus single-pass algorithm, resulting in a significant execution time gain over an existing and popular k-means implementation. Within the context of a variable-size sampling approach, we show that IDDCA can achieve an average CPI error of 1.62% over the 26 SPEC benchmarks, with a maximum error of 5.72% and an average of 403 million instructions.