Sun 18 Jun 2023 10:40 - 11:00 at Magnolia 22 - ISMM: Session 2 - Application Scalability Chair(s): Steve Blackburn

Managed search engines, such as Apache Solr and Elastic-
search, host huge inverted indices in main memory to offer
fast response times. This practice faces two challenges. First,
limited DRAM capacity necessitates search engines aggres-
sively compress indices to reduce their storage footprint.
Unfortunately, our analysis with a popular search library
shows that compression slows down queries (on average) by
up to 1.7× due to high decompression latency. Despite their
performance advantage, uncompressed indices require 10×
more memory capacity, making them impractical. Second,
indices today reside off-heap, encouraging unsafe memory
accesses and risking eviction from the page cache.

Emerging byte-addressable and scalable non-volatile mem-
ory (NVM) offers a good fit for storing uncompressed indices.
Unfortunately, NVM exhibits high latency. We rigorously
evaluate the performance of DRAM and NVM-backed com-
pressed/uncompressed indices to find that an uncompressed
index in a high-capacity managed heap memory-mapped
over NVM provides a 36% reduction in query response times
compared to a DRAM-backed compressed index in off-heap
memory. Also, it is only 11% slower than the uncompressed
index in a DRAM heap (fastest approach). DRAM and NVM-
backed compressed (off-heap) indices behave similarly.

We analyze the narrow response time gap between DRAM
and NVM-backed indices. We conclude that inverted indices
demand massive memory capacity, but search algorithms
exhibit a high spatial locality that modern cache hierarchies
exploit to hide NVM latency. We show the scalability of
uncompressed indices on the NVM-backed heap with large
core counts and index sizes. This work uncovers new space-
time tradeoffs in storing in-memory inverted indices.

Sun 18 Jun

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10:20 - 11:00
ISMM: Session 2 - Application ScalabilityISMM 2023 at Magnolia 22
Chair(s): Steve Blackburn Google and Australian National University

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Scaling Up Performance of Managed Applications on NUMA Systems
ISMM 2023
Orion Papadakis The University of Manchester, Andreas Andronikakis The University of Manchester, Nikos Foutris The University of Manchester, Michail Papadimitriou OctoML, Athanasios Stratikopoulos The University of Manchester, Foivos S. Zakkak Red Hat, Inc., Polychronis Xekalakis Nvidia, Christos Kotselidis Pierer Innovation / The University of Manchester, Foivos S. Zakkak Red Hat, Inc.
Analyzing and Improving the Scalability of In-Memory Indices for Managed Search Engines
ISMM 2023
Aditya Chilukuri Australian National University, Shoaib Akram Australian National University