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Cache and energy efficient algorithms for Nussinov's RNA Folding.

Chunchun Zhao1, Sartaj Sahni2

  • 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. czhao@cise.ufl.edu.

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New cache-efficient algorithms for RNA folding significantly reduce computation time and energy use. ByRow and ByBox algorithms offer substantial performance gains, making RNA secondary structure prediction faster and more energy-efficient.

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Cache efficientNussinov’s algorithmRNA Folding

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithm Design

Background:

  • RNA secondary structure prediction algorithms aim to find optimal non-nested structures by maximizing base pairing and minimizing energy.
  • Nussinov's classical RNA folding algorithm is a foundational method, with various implementations developed to improve efficiency.
  • Reducing cache misses is a key strategy for enhancing the runtime and energy efficiency of RNA folding algorithms.

Purpose of the Study:

  • To develop cache-efficient algorithms for RNA folding based on Nussinov's method.
  • To evaluate the runtime and energy performance of these new algorithms compared to existing ones.
  • To optimize RNA secondary structure prediction for computational speed and energy conservation.

Main Methods:

  • Developed three cache-efficient algorithms: ByRow, ByRowSegment, and ByBox, for RNA folding.
  • Analyzed cache miss rates using a Least Recently Used (LRU) cache model.
  • Conducted extensive experiments on diverse computational platforms (Xeon E5, AMD Athlon 64 X2, Intel I7, PowerPC A2) and programming languages (C, Java).

Main Results:

  • The developed ByRow, ByRowSegment, and ByBox algorithms demonstrated superior cache efficiency over Classical and Transpose algorithms.
  • Experiments confirmed that these cache-efficient algorithms also achieve significant improvements in runtime and energy consumption.
  • Performance gains were substantial, with C versions reducing runtime by up to 97.2% and energy by up to 88.8% compared to Classical.

Conclusions:

  • The ByRow or ByBox algorithms provide the best runtime and energy performance, contingent on the computational platform and programming language.
  • These algorithms offer significant speedups and energy savings, especially in their C implementations, outperforming both Classical and Transpose methods.
  • Unlike Transpose, which requires more memory, ByRow, ByRowSegment, and ByBox use memory comparable to Classical, enabling larger problem sizes.