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

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 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 algor

SUBMITTER: Saeed F 

PROVIDER: S-EPMC8658624 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature