Publication
Title
DRAM-based acceleration of open modification search in hyperdimensional space
Author
Abstract
Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering changes in spectra, but it expands the search space exponentially. In this work, we propose HyperOMS, an algorithm-hardware co-design for boosted OMS, to cope with the enlarged database and expanded search space. HyperOMS encodes spectral data into binary vectors and performs the efficient OMS in high-dimensional space. We accelerate the HyperOMS algorithm using a DRAM-based PIM accelerator, which combines processing-using-memory and near-memory processing technologies. In order to maximize the parallelization and efficiency of the accelerator, we optimize the data allocation and devise an approximation strategy for similarity computation. Experimental results show that the HyperOMS accelerator yields up to 3.8× speedup and 119W higher energy efficiency compared to running HyperOMS on GPU, and up to 99× speedup and 1984× higher energy efficiency over the state-of-the-art OMS tool, ANN-SoLo 1, while providing comparable search quality to competing tools.
Language
English
Source (journal)
IEEE transactions on computer-aided design of integrated circuits and systems. - New York, N.Y.
Publication
New York, N.Y. : 2024
ISSN
0278-0070
DOI
10.1109/TCAD.2024.3382842
Volume/pages
(2024) , p. 1-14
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 01.04.2024
Last edited 04.04.2024
To cite this reference