A near-storage framework for boosted data preprocessing of mass spectrum clustering

Abstract

Mass spectrometry (MS) has been a key to proteomics and metabolomics due to its unique ability to identify and analyze protein structures. Modern MS equipment generates massive amount of tandem mass spectra with high redundancy, making spectral analysis the major bottleneck in design of new medicines. Mass spectrum clustering is one promising solution as it greatly reduces data redundancy and boosts protein identification. However, state-of-the-art MS tools take many hours to run spectrum clustering. Spectra loading and preprocessing consumes average 82% execution time and energy during clustering. We propose a near-storage framework, MSAS, to speed up spectrum preprocessing. Instead of loading data into host memory and CPU, MSAS processes spectra near storage, thus reducing the expensive cost of data movement. We present two types of accelerators that leverage internal bandwidth at two storage levels: SSD and channel. The accelerators are optimized to match the data rate at each storage level with negligible overhead. Our results demonstrate that the channel-level design yields the best performance improvement for preprocessing - it is up to 187X and 1.8X faster than the CPU and the state-of-the-art in-storage computing solution, INSIDER, respectively. After integrating channel-level MSAS into existing MS clustering tools, we measure system level improvements in speed of 3.5X to 9.8X with 2.8X to 11.9X better energy efficiency.