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DTSTAMP:20260522T150115Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post224@linklings.com
SUMMARY:Enabling High-Level Graph Processing via Dynamic Tasking
DESCRIPTION:Maurizio Drocco, Vito Giovanni Castellana, Marco Minutoli, Ant
 onino Tumeo, and John Feo (Pacific Northwest National Laboratory)\n\nData-
 intensive computing yields irregular and unbalanced workloads, in particul
 ar on large-scale problems running on distributed systems. Task-based runt
 ime systems are commonly exploited to implement higher-level data-centric 
 programming models, promoting multithreading and asynchronous coordination
  for performance. However, coping with dynamic workloads (e.g., those yiel
 ded by large-scale graph processing) is challenging.\n\nIn this work, we t
 ook an exploratory approach to overcome some typical bottlenecks in taskin
 g systems. In particular, we propose 1. a novel task allocator based on dy
 namic per-thread allocation and all-to-all recycling networks, and 2. a re
 servation-free remote spawning schema, based on receiver-side buffering an
 d back-pressure feedback/sensing to avoid overflows.\n\nAs a proof of conc
 ept, we implemented the proposed techniques underneath a high-level librar
 y of distributed C++ containers. Preliminary experimental evaluation shows
  consistent scalability, a neat improvement in performance (e.g., 1.5x spe
 edup with respect to the original code over an 8M-nodes graph), and less s
 ensitiveness to parameter tuning.\n\nRegistration Category: Tech Program R
 eg Pass, Exhibits Reg Pass\n\n
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