SIMD-accelerated regular expression matching.

String processing tasks are common in analytical queries powering business intelligence. Besides substring matching, provided in SQL by the like operator, popular DBMSs also support regular expressions as selective filters. Substring matching can be optimized by using specialized SIMD instructions on mainstream CPUs, reaching the performance of numeric column scans. However, generic regular expressions are harder to evaluate, being dependent on both the DFA size and the irregularity of the input. Here, we optimize matching string columns against regular expressions using SIMD-vectorized code. Our approach avoids accessing the strings in lockstep without branching, to exploit cases when some strings are accepted or rejected early by looking at the first few characters. On common string lengths, our implementation is up to 2X faster than scalar code on a mainstream CPU and up to 5X faster on the Xeon Phi co-processor, improving regular expression support in DBMSs.