Arun Subramaniyan
ABSTRACT
Finite State Machines (FSM) are widely used computation models for many application domains. These embarrassingly sequential applications with irregular memory access patterns perform poorly on conventional von-Neumann architectures. The Micron Automata Processor (AP) is an in-situ memory-based computational architecture that accelerates non-deterministic finite automata (NFA) processing in hardware. However, each FSM on the AP is processed sequentially, limiting potential speedups.
In this paper, we explore the FSM parallelization problem in the context of the AP. Extending classical parallelization techniques to NFAs executing on AP is non-trivial because of high state-transition tracking overheads and exponential computation complexity. We present the associated challenges and propose solutions that leverage both the unique properties of the NFAs (connected components, input symbol ranges, convergence, common parent states) and unique features in the AP (support for simultaneous transitions, low-overhead flow switching, state vector cache) to realize parallel NFA execution on the AP.
We evaluate our techniques against several important benchmarks including NFAs used for network intrusion detection, malware detection, text processing, protein motif searching, DNA sequencing, and data analytics. Our proposed parallelization scheme demonstrates significant speedup (25.5x on average) compared to sequential execution on AP. Prior work has already shown that sequential execution on AP is at least an order of magnitude better than GPUs, multi-core processors and Xeon Phi accelerator.
- Micron Automata Processing. Retrieved May 3, 2017 from http://www.micronautomata.com/Google Scholar
- Micron Automata Processing D480 Documentation Design Notes. Retrieved May 3, 2017 from http://www.micronautomata.com/documentation/anml_documentation/c_D480_design_notes.htmlGoogle Scholar
- Micron Automata Processing D480 Software Development Kit. AP Flow Concepts. Retrieved May 3, 2017 from http://micronautomata.com/apsdk_documentation/latest/h1_ap.htmlGoogle Scholar
- Alfred V. Aho and Margaret J. Corasick. 1975. Efficient String Matching: An Aid to Bibliographic Search. Commun. ACM 18, 6 (June 1975), 333--340. Google ScholarDigital Library
- Rajeev Alur and Mihalis Yannakakis. 1998. Model checking of hierarchical state machines. In ACM SIGSOFT Software Engineering Notes, Vol. 23. ACM, 175--188. Google ScholarDigital Library
- Kevin Angstadt, Westley Weimer, and Kevin Skadron. 2016. RAPID Programming of Pattern-Recognition Processors. In Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. ACM, 593--605. Google ScholarDigital Library
- Michela Becchi and Patrick Crowley. 2008. Efficient regular expression evaluation: theory to practice. In Proceedings of the 2008 ACM/IEEE Symposium on Architecture for Networking and Communications Systems, ANCS 2008, San Jose, California, USA, November 6-7, 2008. 50--59. Google ScholarDigital Library
- Michela Becchi, Mark A. Franklin, and Patrick Crowley. 2008. A workload for evaluating deep packet inspection architectures. In 4th International Symposium on Workload Characterization (IISWC 2008), Seattle, Washington, USA, September 14-16, 2008. 79--89.Google ScholarCross Ref
- Chunkun Bo, Ke Wang, Jeffrey J Fox, and Kevin Skadron. 2015. Entity Resolution Acceleration using Micron's Automata Processor. Architectures and Systems for Big Data (ASBD), in conjunction with ISCA (2015).Google Scholar
- Alessandro Cimatti, Edmund Clarke, Enrico Giunchiglia, Fausto Giunchiglia, Marco Pistore, Marco Roveri, Roberto Sebastiani, and Armando Tacchella. 2002. Nusmv 2: An opensource tool for symbolic model checking. In International Conference on Computer Aided Verification. Springer, 359--364. Google ScholarDigital Library
- Sutapa Datta and Subhasis Mukhopadhyay. 2015. A grammar inference approach for predicting kinase specific phosphorylation sites. PloS one 10, 4 (2015), e0122294.Google ScholarCross Ref
- Paul Dlugosch, Dave Brown, Paul Glendenning, Michael Leventhal, and Harold Noyes. 2014. An efficient and scalable semiconductor architecture for parallel automata processing. IEEE Transactions on Parallel and Distributed Systems 25, 12 (2014), 3088--3098.Google ScholarCross Ref
- Domenico Ficara, Stefano Giordano, Gregorio Procissi, Fabio Vitucci, Gianni Antichi, and Andrea Di Pietro. 2008. An improved DFA for fast regular expression matching. ACM SIGCOMM Computer Communication Review 38, 5 (2008), 29--40. Google ScholarDigital Library
- Linley Gwennap. 2014. Micron Accelerates Automata:New Chip Speeds NFA Processing Using DRAM Architectures. In Microprocessor Report.Google Scholar
- W Daniel Hillis and Guy L Steele Jr. 1986. Data parallel algorithms. Commun. ACM 29, 12 (1986), 1170--1183. Google ScholarDigital Library
- Tommy Tracy II, Yao Fu, Indranil Roy, Eric Jonas, and Paul Glendenning. 2016. Towards Machine Learning on the Automata Processor. In High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, Vol. 9697. Springer, 200.Google Scholar
- Christopher Grant Jones, Rose Liu, Leo Meyerovich, Krste Asanovic, and Rastislav Bodik. 2009. Parallelizing the web browser. In Proceedings of the First USENIX Workshop on Hot Topics in Parallelism. Google ScholarDigital Library
- Christopher Grant Jones, Rose Liu, Leo Meyerovich, Krste Asanović, and Rastislav Bodík. 2009. Parallelizing the Web Browser. In Proceedings of the First USENIX Conference on Hot Topics in Parallelism (HotPar'09). USENIX Association, Berkeley, CA, USA, 7--7. http://dl.acm.org/citation.cfm?id=1855591.1855598 Google ScholarDigital Library
- Blake Kaplan. Speculative parsing path. Bug 527623. Retrieved May 3, 2017 from http://bugzilla.mozilla.orgGoogle Scholar
- Shmuel Tomi Klein and Yair Wiseman. 2003. Parallel Huffman decoding with applications to JPEG files. Comput. J. 46, 5 (2003), 487--497.Google ScholarCross Ref
- Sailesh Kumar, Sarang Dharmapurikar, Fang Yu, Patrick Crowley, and Jonathan Turner. 2006. Algorithms to accelerate multiple regular expressions matching for deep packet inspection. In ACM SIGCOMM Computer Communication Review, Vol. 36. ACM, 339--350. Google ScholarDigital Library
- Richard E Ladner and Michael J Fischer. 1980. Parallel prefix computation. Journal of the ACM (JACM) 27, 4 (1980), 831--838. Google ScholarDigital Library
- Daniel Luchaup, Randy Smith, Cristian Estan, and Somesh Jha. 2009. Multi-byte regular expression matching with speculation. In International Workshop on Recent Advances in Intrusion Detection. Springer, 284--303. Google ScholarDigital Library
- Sasa Misailovic, Michael Carbin, Sara Achour, Zichao Qi, and Martin C Rinard. 2014. Chisel: Reliability-and accuracy-aware optimization of approximate computational kernels. In ACM SIGPLAN Notices, Vol. 49. ACM, 309--328. Google ScholarDigital Library
- Todd Mytkowicz, Madanlal Musuvathi, and Wolfram Schulte. 2014. Data-parallel finite-state machines. In Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, Salt Lake City, UT, USA, March 1-5, 2014. 529--542. Google ScholarDigital Library
- Alexandre Petrenko. 2001. Fault model-driven test derivation from finite state models: Annotated bibliography. In Modeling and verification of parallel processes. Springer, 196--205. Google ScholarDigital Library
- Junqiao Qiu, Zhijia Zhao, and Bin Ren. 2016. MicroSpec: Speculation-Centric Fine-Grained Parallelization for FSM Computations. In Proceedings of the 2016 International Conference on Parallel Architectures and Compilation, PACT 2016, Haifa, Israel, September 11-15, 2016. 221--233. Google ScholarDigital Library
- Indranil Roy and Srinivas Aluru. 2016. Discovering motifs in biological sequences using the micron automata processor. IEEE/ACM Transactions on Computational Biology and Bioinformatics 13, 1 (2016), 99--111.Google ScholarDigital Library
- Margus Veanes, Todd Mytkowicz, David Molnar, and Benjamin Livshits. 2015. Data-Parallel String-Manipulating Programs. In Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2015, Mumbai, India, January 15-17, 2015. 139--152. Google ScholarDigital Library
- Jack Wadden, Nathan Brunelle, Ke Wang, Mohamed El-Hadedy, Gabriel Robins, Mircea Stan, and Kevin Skadron. 2016. Generating efficient and high-quality pseudo-random behavior on Automata Processors. In 34th IEEE International Conference on Computer Design, ICCD 2016, Scottsdale, AZ, USA, October 2-5, 2016. 622--629.Google ScholarCross Ref
- Jack Wadden, Vinh Dang, Nathan Brunelle, Tommy Tracy II, Deyuan Guo, Elaheh Sadredini, Ke Wang, Chunkun Bo, Gabriel Robins, Mircea Stan, and Kevin Skadron. 2016. ANMLzoo: a benchmark suite for exploring bottlenecks in automata processing engines and architectures. In 2016 IEEE International Symposium on Workload Characterization, IISWC 2016, Providence, RI, USA, September 25-27, 2016. 105--166.Google ScholarCross Ref
- Ke Wang, Yanjun Qi, Jeffrey J Fox, Mircea R Stan, and Kevin Skadron. 2015. Association rule mining with the micron automata processor. In Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE International. IEEE, 689--699. Google ScholarDigital Library
- Ke Wang, Elaheh Sadredini, and Kevin Skadron. 2016. Sequential pattern mining with the Micron automata processor. In Proceedings of the ACM International Conference on Computing Frontiers. ACM, 135--144. Google ScholarDigital Library
- Michael HLS Wang, Gustavo Cancelo, Christopher Green, Deyuan Guo, Ke Wang, and Ted Zmuda. 2016. Using the automata processor for fast pattern recognition in high energy physics experiments--A proof of concept. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 832 (2016), 219--230.Google ScholarCross Ref
- Qiong Wang, Mohamed El-Hadedy, Ke Wang, and Kevin Skadron. 2016. Accelerating Weeder: A DNA Motif Search Tool using the Micron Automata Processor. (2016).Google Scholar
- Zhen-Gang Wang, Johann Elbaz, Françoise Remacle, RD Levine, and Itamar Willner. 2010. All-DNA finite-state automata with finite memory. Proceedings of the National Academy of Sciences 107, 51 (2010), 21996--22001.Google ScholarCross Ref
- Yi-Hua E Yang and Viktor K Prasanna. 2011. Optimizing regular expression matching with sr-nfa on multi-core systems. In Parallel Architectures and Compilation Techniques (PACT), 2011 International Conference on. IEEE, 424--433. Google ScholarDigital Library
- Fang Yu, Zhifeng Chen, Yanlei Diao, TV Lakshman, and Randy H Katz. 2006. Fast and memory-efficient regular expression matching for deep packet inspection. In Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems. ACM, 93--102. Google ScholarDigital Library
- Zhijia Zhao and Xipeng Shen. 2015. On-the-Fly Principled Speculation for FSM Parallelization. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '15, Istanbul, Turkey, March 14-18, 2015. 619--630. Google ScholarDigital Library
- Zhijia Zhao, Bo Wu, and Xipeng Shen. 2014. Challenging the "embarrassingly sequential": parallelizing finite state machine-based computations through principled speculation. In Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, Salt Lake City, UT, USA, March 1-5, 2014. 543--558. Google ScholarDigital Library
- Keira Zhou, Jeffrey J Fox, Ke Wang, Donald E Brown, and Kevin Skadron. 2015. Brill tagging on the micron automata processor. In Semantic Computing (ICSC), 2015 IEEE International Conference on. IEEE, 236--239.Google ScholarCross Ref
Index Terms
- Parallel Automata Processor
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