Molecular biologists use hidden Markov models (HMMs) as a popular tool to 
statistically describe biological sequence families. This statistical 
description can then be used for sensitive and selective database scanning, 
e.g., new protein sequences are compared with a set of HMMs to detect functional 
similarities. Efficient dynamic-programming algorithms exist for solving this 
problem; however, current solutions still require significant scan times. These 
scan time requirements are likely to become even more severe due to the rapid 
growth in the size of these databases. This paper shows how reconfigurable 
architectures can be used to derive an efficient fine-grained parallelization of 
the dynamic programming calculation. We describe how this technique leads to 
significant runtime savings for HMM database scanning on a standard 
off-the-shelf field-programmable gate array (FPGA).