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Analysing the performance of personal computers based on Intel microprocessors for sequence aligning bioinformatics

Pradeep S Nair1, Eugene B John

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Bioinformatics sequence alignment on personal computers using BLAST and FASTA shows performance gains are possible. Optimizing the L1-cache can significantly improve the speed of these crucial bioinformatics benchmarks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Computer Science

Background:

  • Sequence alignment is fundamental in bioinformatics.
  • Personal computers are increasingly used for local sequence alignment.
  • Performance analysis of bioinformatics tools on local hardware is essential.

Purpose of the Study:

  • To analyze the performance of personal computers for sequence alignment.
  • To evaluate popular bioinformatics benchmarks like BLAST and FASTA.
  • To identify potential performance bottlenecks and optimization strategies.

Main Methods:

  • Benchmarking personal computer performance.
  • Analyzing the computational and memory operations of BLAST and FASTA.
  • Identifying recurring operations within the alignment algorithms.

Main Results:

  • Sequence alignment benchmarks exhibit numerous recurring operations.
  • Extensive use of memory operations was observed.
  • Performance appears to be sensitive to L1-cache size.

Conclusions:

  • Personal computer performance for sequence alignment is influenced by hardware factors like L1-cache.
  • Optimizing cache memory could enhance the efficiency of bioinformatics sequence alignment.
  • Further research into hardware-specific optimizations for bioinformatics tools is warranted.