Case study of a functional genomics application for an FPGA-based coprocessor
Although microarrays are already having a tremendous impact on biomedical science, they still present great computational challenges. We examine a particular problem involving the computation of linear regressions on a large number of vector combinations in a high-dimensional parameter space, a problem that was found to be virtually intractable on a PC cluster. We observe that characteristics of this problem map particularly well to FPGAs and confirm this with an implementation that results in a 1600-fold speed-up over an optimized serial implementation. Some of the other contributions involve the data routing structure, the analysis of bit-width allocation, and the handling of missing data. Since this problem is representative of many in functional genomics, part of the overall significance of this work is that it points to a new area of applicability for FPGA coprocessors. (C) 2004 Elsevier B.V. All rights reserved.
computational coprocessor fpga application bioinformatics microarray data analysis steiner system