SkimROOT: Accelerating LHC Data Filtering with Near-Storage Processing

Abstract: Data analysis in high-energy physics (HEP) begins with data reduction, where vast datasets are filtered to extract relevant events. At the Large Hadron Collider (LHC), this process is bottlenecked by slow data transfers between storage and compute nodes. To address this, we introduce SkimROOT, a near-data filtering system leveraging Data Processing Units (DPUs) to accelerate LHC data analysis. By performing filtering directly on storage servers and returning only the relevant data, SkimROOT minimizes data movement and reduces processing delays. Our prototype demonstrates significant efficiency gains, achieving a 44.3$\times$ performance improvement, paving the way for faster physics discoveries.