Benchmarking genetically improved BarraCUDA on epigenetic methylation NGS datasets and nVidia GPUs

BarraCUDA uses CUDA graphics cards to map DNA reads to the human genome. Previously its software source code was genetically improved for short paired end next generation sequences. On longer noisy epigenetics strings using nVidia Titan and twin Tesla K40 the same GI-ed code is more than 3 times faster than bwa-meth on an 8 core CPU

Improving SSE parallel code with grow and graft genetic programming

RNAfold predicts the secondary structure of RNA molecules from their base sequence. We apply a mixture of manual and automated genetic improvements to its C source. GI gives a 1.6% improvement to parallel SSE4.1 code. The automatic programming evolutionary system has access to Intel library code and previous revisions. On 4 666 curated structures from RNA STRAND, GGGP gives a combined speed up of 31.9%, with no loss of accuracy (GI code run 1:4 1011 times)