Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Families of FPGA-Based Algorithms for Approximate String Matching
ASAP '04 Proceedings of the Application-Specific Systems, Architectures and Processors, 15th IEEE International Conference
Hyper customized processors for bio-sequence database scanning on FPGAs
Proceedings of the 2005 ACM/SIGDA 13th international symposium on Field-programmable gate arrays
Exploiting Coarse-Grained Parallelism to Accelerate Protein Motif Finding with a Network Processor
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
ClawHMMER: A Streaming HMMer-Search Implementatio
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
MPI-HMMER-Boost: Distributed FPGA Acceleration
Journal of VLSI Signal Processing Systems
Visions for application development on hybrid computing systems
Parallel Computing
Integrating FPGA acceleration into HMMer
Parallel Computing
HSP-HMMER: a tool for protein domain identification on a large scale
Proceedings of the 2009 ACM symposium on Applied Computing
Strategies for dynamic memory allocation in hybrid architectures
Proceedings of the 6th ACM conference on Computing frontiers
Efficient memory management for hardware accelerated Java Virtual Machines
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Hardware Acceleration of HMMER on FPGAs
Journal of Signal Processing Systems
Accelerating HMMER on GPUs by implementing hybrid data and task parallelism
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A HMMER hardware accelerator using divergences
Proceedings of the Conference on Design, Automation and Test in Europe
Autotuned parallel I/O for highly scalable biosequence analysis
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
A protein sequence analysis hardware accelerator based on divergences
International Journal of Reconfigurable Computing - Special issue on High-Performance Reconfigurable Computing
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Profile Hidden Markov models (HMMs) are a powerful approach to describing biologically significant functional units, or motifs, in protein sequences. Entire databases of such models are regularly compared to large collections of proteins to recognize motifs in them. Exponentially increasing rates of genome sequencing have caused both protein and model databases to explode in size, placing an ever-increasing computational burden on users of these systems.Here, we describe an accelerated search system that exploits parallelism in a number of ways. First, the application is functionally decomposed into a pipeline, with distinct compute resources executing each pipeline stage. Second, the first pipeline stage is deployed on a systolic array, which yields significant fine-grained parallelism. Third, for some instantiations of the design, parallel copies of the first pipeline stage are used, further increasing the level of coarse-grained parallelism.A naïve parallelization of the first stage computation has serious repercussions for the sensitivity of the search. We present a pair of remedies to this dilemma and quantify the regions of interest within which each approach is most effective. Analytic performance models are used to assess the overall speedup that can be attained relative to a single-processor software solution. Performance improvements of 1 to 2 orders of magnitude are predicted.