  Database-search algorithms, that deduce peptides from Mass Spectrometry (MS)
data, have tried to improve the computational efficiency to accomplish larger,
and more complex systems biology studies. Existing serial, and high-performance
computing (HPC) search engines, otherwise highly successful, are known to
exhibit poor-scalability with increasing size of theoretical search-space
needed for increased complexity of modern non-model, multi-species MS-based
omics analysis. Consequently, the bottleneck for computational techniques is
the communication costs of moving the data between hierarchy of memory, or
processing units, and not the arithmetic operations. This post-Moore change in
architecture, and demands of modern systems biology experiments have dampened
the overall effectiveness of the existing HPC workflows. We present a novel
efficient parallel computational method, and its implementation on
memory-distributed architectures for peptide identification tool called HiCOPS,
that enables more than 100-fold improvement in speed over most existing HPC
proteome database search tools. HiCOPS empowers the supercomputing database
search concept for comprehensive identification of peptides, and all their
modified forms within a reasonable time-frame. We demonstrate this by searching
Gigabytes of experimental MS data against Terabytes of databases where HiCOPS
completes peptide identification in few minutes using 72 parallel nodes (1728
cores) compared to several weeks required by existing state-of-the-art tools
using 1 node (24 cores); 100 minutes vs 5 weeks; 500x speedup. Finally, we
formulate a theoretical framework for our overhead-avoiding strategy, and
report superior performance evaluation results for key metrics including
execution time, CPU utilization, speedups, and I/O efficiency. The software
will be made available at: hicops.github.io
