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DTSTART;TZID=America/New_York:20241121T113000
DTEND;TZID=America/New_York:20241121T120000
UID:submissions.supercomputing.org_SC24_sess376_pap461@linklings.com
SUMMARY:CARP: Range Query-Optimized Indexing for Streaming Data
DESCRIPTION:Ankush Jain and Chuck Cranor (Carnegie Mellon University), Qin
 g Zheng (Los Alamos National Laboratory (LANL)), Brad Settlemyer (NVIDIA C
 orporation), George Amvrosiadis (Carnegie Mellon University), and Gary Gri
 der (Los Alamos National Laboratory (LANL))\n\nIngestion of data generated
  by high-performance scientific applications continues to stress available
  storage resources. Efficient range-based analyses on this data can be ena
 bled by reordering it on attributes of interest, but require expensive pos
 t-processing sorts to realize the query benefits of reordering. In-situ in
 dexing techniques, while write-efficient, are orders of magnitude slower a
 t range queries than sorted indices. Range queries are necessary for analy
 zing continuous physical attributes and tracking phenomena such as energy 
 bands and wave fronts. \n\nWe present CARP, a scalable data partitioner fo
 r range queries that reorders data in-situ as it is streamed to storage du
 ring application I/O. Motivated by our findings that real application dist
 ributions tend to be highly skewed and dynamic, CARP dynamically discovers
  and adapts its data partitions to track these characteristics. As a resul
 t, CARP can approximate the query performance of a sort without any ingest
 ion overhead, making it 5X faster than prior work.\n\nTag: Algorithms, Dat
 a Movement and Memory, I/O, Storage, Archive, Performance Optimization, Sc
 ientific and Information Visualization, Visualization\n\nRegistration Cate
 gory: Tech Program Reg Pass\n\nSession Chair: Rosa Filgueira (University o
 f St Andrews, Scotland)\n\n
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