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Study of Energy and Compression-Ratio Tradeoff in Portable Sequencers

Sojoodi, Hossein | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53943 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Goudarzi, Maziar
  7. Abstract:
  8. Recently, portable genome sequencing devices have been introduced to the market, which have also made it possible to provide these services in remote locations or outside the laboratory. The amount of raw data from the readings of a sequencer for the entire genome of a human or plant can be in the hundreds of gigabytes, making it difficult and expensive to maintain and transfer to the center for such sequencing. Fortunately, these readings have a lot of redundancy, and many new algorithms have been proposed to compress them based on the intrinsic properties of this data. Sequencing devices were mainly used in the laboratory environment, which naturally had virtually unlimited access to urban electricity. Therefore, the proposed algorithms do not pay attention to the energy problem. But with the advent of portable sequencing, the issue of energy consumption versus compression has become a new trade-off. In this research, we first study and review the latest algorithms presented and then, by defining a new criterion, we have compared these algorithms in terms of energy consumption and compression rate. Then, based on the obtained information, we have provided solutions for the optimal use of existing algorithms. In the following, by observing the effective factors in energy consumption, including the rate of cache memory and accurate analysis of energy consumption in different parts of the HARC algorithm, we have presented and evaluated solutions to reduce the energy consumption of this algorithm and thus improve the "Joule per reduced volume" criterion.The results show that using the proposed solutions, the first solution up to 88% and the second solution up to 47% have improved the performance of the HARC algorithm from the point of view of the introduced benchmark
  9. Keywords:
  10. Compression ; Energy ; Optimization ; Coverage Depth ; Energy-Performance Trade-off ; Genome Sequencing

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