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Generating Memory Allocators from the Ground Up
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AI
DOI：10.1007/978-3-032-02436-7_11.png)
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Abstract
En 中文
General-purpose memory allocators are made to perform well on average for any given program. They thus make decisions which can benefit a broad set of applications and can miss out on possible optimizations. When a given general-purpose allocator does not fit the needs of a program, the developer has a choice of either switching to a different allocator or writing a custom one from scratch. Both options can be quite costly, and can still fail to satisfy the developer's requirements. We propose a different approach to memory allocation: allocators are automatically generated from the ground up for any given program and optimized for the needed metric. We outline metrics of allocator performance, present a taxonomy of single-threaded memory allocators, and a framework for generating custom allocators based on the taxonomy. We show that allocators generated in such way can match or outperform general-purpose allocators and that different applications benefit from different components of our taxonomy.
Keywords:
Memory allocation
Program synthesis
Journal
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Papers:
14
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