Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data

TL;DR

This paper establishes fundamental communication lower bounds for parallel algorithms in mass spectrometry-based omics data analysis, highlighting the gap in current methods and emphasizing the need for communication-efficient algorithms for large-scale biological data processing.

Contribution

It proves the theoretical communication bounds for existing and optimal parallel algorithms in proteomics, and validates these bounds through analysis and experiments, urging development of provably optimal methods.

Findings

Existing algorithms do not achieve the communication lower bounds.

Optimal strategies can significantly reduce communication costs.

Current methods exhibit sub-optimal speedups with more processors.

Abstract

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 \emph{existing} parallel algorithms is $\Omega(mn+2r\frac{q}{p})$, 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 $\sqrt{M} = mn + \frac{2qr}{p}$ can achieve $\Omega({\frac{2mnq}{p}})$ 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 \emph{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.