Cross-Domain Acceleration of Open Modification Search: From Commodity Platforms to Emerging Memory and Storage Devices

2026-07-20

AI Summary

This work addresses the bottleneck in open modification search (OMS) for mass spectrometry, which is constrained more by data movement than computation, and the lack of systematic cross-platform evaluation of existing accelerators. It proposes the first lightweight similarity assessment method based on binary hyperdimensional computing (HDC), transforming core computations into efficient bitwise operations. Under unified algorithmic and precision assumptions, the study conducts a workload-driven cross-platform evaluation across diverse architectures—including GPUs, near-memory FPGAs, processing-in-DRAM, ReRAM/PCM-based in-memory computing, and 3D NAND/FeNAND in-storage processing. Results demonstrate that memory- and storage-centric architectures achieve over two orders of magnitude speedup and more than 40,000× improvement in energy efficiency, revealing a critical pathway toward scalable, high-throughput OMS.

Abstract

Open modification search (OMS) in mass spectrometry (MS) is a data-intensive workload whose performance is dominantly limited by reference data movement rather than computation. Prior OMS accelerators have largely been evaluated in isolation, making it difficult to understand system-level trade-offs across platforms. This paper presents the first workload-driven, cross-platform survey of accelerators for MS search by studying not only commodity platforms, but also emerging memory- and storage-centric architectures, including GPUs, near-storage FPGAs, DRAM near-memory processing, ReRAM/PCM in-memory processing, and 3D NAND/FeNAND in-storage processing, under consistent algorithmic and accuracy assumptions. Leveraging a binary hyperdimensional computing (HDC)-based OMS formulation that reduces similarity evaluation to lightweight bitwise primitives and tolerates device-level non-idealities, we enable a robust execution on memory-centric architectures despite device-level non-idealities and limited computing capability. Overall, this study identifies memory- and storage-centric architectures as a key architectural breakthrough for large-scale, high-speed search acceleration, delivering up to >100x speedup and >40,000x improvement in energy efficiency.

Problem

• Open Modification Search
• Mass Spectrometry
• Cross-Platform Acceleration
• Memory-Centric Architectures
• Data Movement Bottleneck

Innovation

• open modification search
• hyperdimensional computing
• in-memory processing
• near-storage computing
• cross-platform acceleration