{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T18:45:46Z","timestamp":1776969946665,"version":"3.51.4"},"reference-count":0,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMOD Rec."],"published-print":{"date-parts":[[2026,4,23]]},"abstract":"<jats:p>\n                    ML deployments in real-world settings are often heterogeneous, spanning local multi-core CPU operations, GPU-accelerated computations, and distributed execution on platforms such as Apache Spark. This heterogeneity arises from practical necessity: a feature engineering step might run locally, a large matrix multiplication offloads to Spark when data exceeds driver memory, and DNN layers execute on GPUs. Or an initial homogeneous setup might evolve over time into a heterogeneous one. This multi-backend reality creates a systems challenge that has received surprisingly little research attention:\n                    <jats:italic toggle=\"yes\">How to holistically manage computation reuse and memory across backends with fundamentally different execution models, memory hierarchies, and caching primitives?<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1145\/3810900.3810915","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T18:16:38Z","timestamp":1776968198000},"page":"84-84","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Technical Perspective on 'MEMPHIS: Holistic Lineage-based Reuse and Memory Management for Multi-backend ML Systems'"],"prefix":"10.1145","volume":"55","author":[{"given":"Arun","family":"Kumar","sequence":"first","affiliation":[{"name":"University of California, San Diego and RapidFire AI, Inc., San Diego, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,23]]},"container-title":["ACM SIGMOD Record"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3810900.3810915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T18:16:44Z","timestamp":1776968204000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3810900.3810915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,4,23]]}},"alternative-id":["10.1145\/3810900.3810915"],"URL":"https:\/\/doi.org\/10.1145\/3810900.3810915","relation":{},"ISSN":["0163-5808"],"issn-type":[{"value":"0163-5808","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]},"assertion":[{"value":"2026-04-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}