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Background  An RNA folding/RNA secondary structure prediction algorithm determines the non-nested/pseudoknot-free structure by maximizing the number of complementary base pairs and minimizing the energy. Several implementations of Nussinov’s classical RNA folding algorithm have been proposed. Our focus is to obtain run time and energy efficiency by reducing the number of cache misses.    Results  Three cache-efficient algorithms,  ByRow ,  ByRowSegment  and  ByBox , for Nussinov’s RNA folding are developed. Using a simple LRU cache model, we show that the  Classical  algorithm of Nussinov has the highest number of cache misses followed by the algorithms  Transpose  (Li et al.),  ByRow ,  ByRowSegment , and  ByBox  (in this order). Extensive experiments conducted on four computational platforms–Xeon E5, AMD Athlon 64 X2, Intel I7 and PowerPC A2–using two programming languages–C and Java–show that our cache efficient algorithms are also efficient in terms of run time and energy.    Conclusion  Our benchmarking shows that, depending on the computational platform and programming language, either  ByRow  or  ByBox  give best run time and energy performance. The C version of these algorithms reduce run time by as much as 97.2 %  and energy consumption by as much as 88.8 %  relative to  Classical  and by as much as 56.3 %  and 57.8 %  relative to  Transpose . The Java versions reduce run time by as much as 98.3 %  relative to  Classical  and by as much as 75.2 %  relative to  Transpose .  Transpose  achieves run time and energy efficiency at the expense of memory as it takes twice the memory required by  Classical . The memory required by  ByRow ,  ByRowSegment , and  ByBox  is the same as that of  Classical . As a result, using the same amount of memory, the algorithms proposed by us can solve problems up to 40% larger than those solvable by  Transpose .

Cache and energy efficient algorithms for Nussinov’s RNA Folding