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DTSTART;TZID=America/Chicago:20251117T140000
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UID:submissions.supercomputing.org_SC25_sess217_ws_drbsd112@linklings.com
SUMMARY:FZModules: A Heterogeneous Computing Framework for Customizable Sc
 ientific Data Compression Pipelines
DESCRIPTION:Skyler Ruiter (Indiana University), Jiannan Tian (Oakland Univ
 ersity), and Fengguang Song (Indiana University)\n\nModern scientific simu
 lations and instruments generate data volumes that overwhelm memory and st
 orage, throttling scalability. Lossy compression mitigates this by trading
  controlled error for reduced footprint and throughput gains, yet optimal 
 pipelines are highly data and objective specific, demanding compression ex
 pertise. GPU compressors supply raw throughput but often hard‑code fused k
 ernels that hinder rapid experimentation, and underperform in rate–distort
 ion. We present FZModules, a heterogeneous framework for assembling error‑
 bounded custom compression pipelines from high‑performance modules through
  a concise extensible interface. We further utilize an asynchronous task-b
 acked execution library that infers data dependencies, manages memory move
 ment, and exposes branch and stage level concurrency for powerful asynchro
 nous compression pipelines. Evaluating three pipelines built with FZModule
 s on four representative scientific datasets, we show they can compare end
 ‑to‑end speedup of fused‑kernel GPU compressors while achieving similar ra
 te–distortion to higher fidelity CPU or hybrid compressors, enabling rapid
 , domain-tailored design.\n\nRecording: Livestreamed, Recorded\n\nRegistra
 tion Category: Technical Program Reg Pass, Workshop Reg Pass\n\nSession Ch
 airs: Sheng Di (Argonne National Laboratory (ANL), University of Chicago);
  Ana Gainaru (Oak Ridge National Laboratory (ORNL)); Kento Sato (RIKEN Cen
 ter for Computational Science (R-CCS)); Xin Liang (University of Kentucky)
 ; and Jieyang Chen (University of Oregon)\n\n
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