Learning-based Memory Allocation for C++ Server Workloads Article Swipe
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Martin Maas
,
David G. Andersen
,
Michael Isard
,
Mohammad Mahdi Javanmard
,
Kathryn S. McKinley
,
Colin Raffel
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3373376.3378525
· OA: W3011083606
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3373376.3378525
· OA: W3011083606
Modern C++ servers have memory footprints that vary widely over time, causing persistent heap fragmentation of up to 2x from long-lived objects allocated during peak memory usage. This fragmentation is exacerbated by the use of huge (2MB) pages, a requirement for high performance on large heap sizes. Reducing fragmentation automatically is challenging because C++ memory managers cannot move objects.
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