开放修改搜索的跨域加速：从商品平台到新兴内存和存储设备
Cross-Domain Acceleration of Open Modification Search: From Commodity Platforms to Emerging Memory and Storage Devices
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中文总结 AI 辅助
研究质谱分析中开放修改搜索（OMS）的跨平台加速，涵盖商品及新兴内存存储架构。基于二进制超维计算（HDC）公式，实现稳健执行，确定相关架构为关键突破，大幅提升加速和能源效率。
中文摘要 AI 辅助
质谱分析中的开放修改搜索（OMS）是一种数据密集型工作负载，其性能主要受参考数据移动而非计算的限制。以往的OMS加速器大多是单独评估的，难以理解跨平台的系统级权衡。本文首次进行了工作负载驱动的跨平台质谱搜索加速器调查，研究了商品平台以及新兴的以内存和存储为中心的架构，包括GPU、近存储FPGA、DRAM近内存处理、ReRAM/PCM内存处理以及3D NAND/FeNAND存储处理。利用基于二进制超维计算（HDC）的OMS公式，将相似性评估简化为轻量级按位原语，并容忍设备级非理想情况，实现了在以内存为中心的架构上的稳健执行。总体而言，本研究将以内存和存储为中心的架构确定为大规模、高速搜索加速的关键架构突破，实现了高达100倍以上的加速和40000倍以上的能源效率提升。
英文摘要
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.
