ISMM 2024 – Proceedings

2024 ACM SIGPLAN International Symposium on Memory Management (ISMM 2024)

Frontmatter

Garbage Collection

Garbage Collection for Mostly Serialized Heaps
Chaitanya S. Koparkar, Vidush Singhal, Aditya Gupta, Mike Rainey, Michael Vollmer, Artem Pelenitsyn, Sam Tobin-Hochstadt, Milind Kulkarni, and Ryan R. Newton
(Indiana University, USA; Purdue University, USA; Carnegie Mellon University, USA; University of Kent, United Kingdom)

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Memory Management on Mobile Devices
Kunal Sareen, Stephen M. Blackburn, Sara S. Hamouda, and Lokesh Gidra
(Australian National University, Australia; Google, Australia; Google, USA)

Publisher's Version

Evaluating Finalization-Based Object Lifetime Profiling
Sebastian Jordan Montaño, Guillermo Polito, Stephane Ducasse, and Pablo Tesone
(Univ. Lille - Inria - CNRS - Centrale Lille - UMR 9189 CRIStAL, France)

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Hardware for Memory Management

Supporting Trusted Virtual Machines with Hardware-Based Secure Remote Memory
Taekyung Heo, Seunghyo Kang, Sanghyeon Lee, Soojin Hwang, Joongun Park, and Jaehyuk Huh
(NVIDIA, USA; KAIST, South Korea; Georgia Tech, USA)

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A Managed Memory System for Micro Controllers with NOR Flash Memory
Akira Inoue, Tomoharu Ugawa, and Shigeru Chiba
(University of Tokyo, Japan)

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Potpourri

SSRD: Shapes and Summaries for Race Detection in Concurrent Data Structures
Xiaofan Sun and Rajiv Gupta
(University of California at Riverside, Riverside, USA)

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A Heuristic for Periodic Memory Allocation with Little Fragmentation to Train Neural Networks
Akifumi Imanishi and Zijian Xu
(Preferred Networks, Japan)

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ESPN: Memory-Efficient Multi-vector Information Retrieval
Susav Shrestha, Narasimha Reddy, and Zongwang Li
(Texas A&M University, USA; Samsung, USA)

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Intellectual Abstracts

The One Pass (OP) Compactor: An Intellectual Abstract
Tomer Cory and Erez Petrank
(Technion, Israel)

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BatchIt: Optimizing Message-Passing Allocators for Producer-Consumer Workloads: An Intellectual Abstract
Nathaniel Wesley Filardo and Matthew J. Parkinson
(Microsoft Azure, Canada; Microsoft Azure Research, United Kingdom)

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Reference Counting Deeply Immutable Data Structures with Cycles: An Intellectual Abstract
Matthew J. Parkinson, Sylvan Clebsch, and Tobias Wrigstad
(Microsoft Azure Research, United Kingdom; Microsoft Azure Research, USA; Uppsala University, Sweden)

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