Performance of DNA Sequences Compression on Multicores and GPUs using Adaptive-GenCodexMethod

Ajith Padyana, Pallav Kumar Baruah


The DNA sequences are huge in size and the databases are growing at an exponential rate. For example, the human genome in raw format ranges from 2 to 30 Tera-bytes. The main reason for this is the invention of new species and increasing number of DNA profiles. The growth of the DNA affects the storage as well as bandwidth when these sequences need to be transferred. Applications such as DNA profiling, Real time DNA crime investigation require access to the DNA sequences in real time. The inherent property of DNA is that it contains many repeats which makes it highly compressible. However, the applications mentioned not only require good compression ratio but also needs faster compression. Multicores and GPUs can be used to perform the compression quickly. In this paper, we propose a new algorithm with a focus on the throughput along with the compression ratio. The algorithm scales well on GPUs and achieves a speedup of  11 on multicores and upto 23 on GPUs when run on M2070 Tesla card and upto 57 on K20 Kepler GPUs. We also extended this algorithm such that it adapts to the input  sequence depending on the number of consecutive repeats and accordingly chooses the
right algorithm which leads to a better compression.


DNA Sequence; Bandwidth; Throughput; Compression Ratio; Speedup; Code byte

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