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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-performancecomputing (HPC) search engines, otherwise highly successful, are known toexhibit poor-scalability with increasing size of theoretical search-spaceneeded for increased complexity of modern non-model, multi-species MS-basedomics analysis. Consequently, the bottleneck for computational techniques isthe communication costs of moving the data between hierarchy of memory, orprocessing units, and not the arithmetic operations. This post-Moore change inarchitecture, and demands of modern systems biology experiments have dampenedthe overall effectiveness of the existing HPC workflows. We present a novelefficient parallel computational method, and its implementation onmemory-distributed architectures for peptide identification tool called HiCOPS,that enables more than 100-fold improvement in speed over most existing HPCproteome database search tools. HiCOPS empowers the supercomputing databasesearch concept for comprehensive identification of peptides, and all theirmodified forms within a reasonable time-frame. We demonstrate this by searchingGigabytes of experimental MS data against Terabytes of databases where HiCOPScompletes peptide identification in few minutes using 72 parallel nodes (1728cores) compared to several weeks required by existing state-of-the-art toolsusing 1 node (24 cores); 100 minutes vs 5 weeks; 500x speedup. Finally, weformulate a theoretical framework for our overhead-avoiding strategy, andreport superior performance evaluation results for key metrics includingexecution time, CPU utilization, speedups, and I/O efficiency. The softwarewill be made available at: hicops.github.io

HiCOPS: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry based Omics Data