Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speed-ups of up to 17.6x. Our approach is up to 3x faster than a pattern-based parallel approach that uses the RE2 library.
Intrusion prevention systems (IPSs) determine whether incoming traffic matches a database of signatures, where each signature is a regular expression and represents an attack or a vulnerability. IPSs need to keep up with ever-increasing line speeds, which has lead to the use of custom hardware. A major bottleneck that IPSs face is that they scan incoming packets one byte at a time, which limits their throughput and latency. In this paper, we present a method to search for arbitrary regular expressions by scanning multiple bytes in parallel using speculation. We break the packet in several chunks, opportunistically scan them in parallel, and if the speculation is wrong, correct it later. We present algorithms that apply speculation in single-threaded software running on commodity processors as well as algorithms for parallel hardware. Experimental results show that speculation leads to improvements in latency and throughput in both cases.