Improving Data Locality of RNA Secondary Structure Prediction Code

An approach allowing us to improve the locality of RNA Secondary Structure Prediction code is proposed. We discuss the application of this technique to automatic loop nest tiling for the Nussinov algorithm. The approach requires an exact representation of dependences in the form of tuple relations and calculating the transitive closure of dependence graphs. First, to improve code locality, 3-d rectangular tiles are formed within the 3-d iteration space of the Nussinov loop nest. Then tiles are corrected to establish code validity by means of applying the exact transitive closure of a dependence graph. The approach has been implemented as a part of the polyhedral TRACO compiler. The experimental results presents the speed-up factor of optimized code. Related work and future tasks are outlined.

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