Futureproof Static Memory Planning

• URL: http://arxiv.org/abs/2504.04874v1
• Date: Mon, 07 Apr 2025 09:28:54 GMT
• Title: Futureproof Static Memory Planning
• Authors: Christos Lamprakos, Panagiotis Xanthopoulos, Manolis Katsaragakis, Sotirios Xydis, Dimitrios Soudris, Francky Catthoor,
• Abstract summary: "AI memory wall" combined with deep neural networks' static architecture has reignited interest in dynamic storage allocation.<n>We present idealloc, a low-fragmentation, high-performance DSA implementation designed for million-buffer instances.
• Score: 7.031511274524772
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The NP-complete combinatorial optimization task of assigning offsets to a set of buffers with known sizes and lifetimes so as to minimize total memory usage is called dynamic storage allocation (DSA). Existing DSA implementations bypass the theoretical state-of-the-art algorithms in favor of either fast but wasteful heuristics, or memory-efficient approaches that do not scale beyond one thousand buffers. The "AI memory wall", combined with deep neural networks' static architecture, has reignited interest in DSA. We present idealloc, a low-fragmentation, high-performance DSA implementation designed for million-buffer instances. Evaluated on a novel suite of particularly hard benchmarks from several domains, idealloc ranks first against four production implementations in terms of a joint effectiveness/robustness criterion.

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