Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data - Saeed, Fahad; Haseeb, Muhammad; Iyengar, S.S. (Journal of parallel and distributed computing, 2022)
•We present a theoretical framework that can be used for analyzing, and quantifying the performance of parallel algorithms designed for MS based omics data.•We prove the lower communication bounds for the existing parallel algorithms.•We also prove lower communication bounds that can be theoretically achieved by parallel algorithms for MS based omics analysis.•Extensive experimentation for state of the art tools confirms our theoretical results.•This is first proof of any communication bounds for parallel algorithms for MS based omics. Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the existing parallel algorithms is Ω(mn+2rqp), where m and n are the dimensions of the theoretical database matrix, q and r are dimensions of spectra, and p is the number of processors. We further prove that communication-optimal strategy with fast-memory M=mn+2qrp can achieve Ω(2mnqp) but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of provable, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems.