Our paper, Reconsidering Custom Memory Allocation, was just granted the Most Influential OOPSLA Paper award (given ten years after the paper appeared). Here’s the citation for the award.

Custom memory management is often used in systems software for the purpose of decreasing the cost of allocation and tightly controlling memory footprint of software. Until 2002, it was taken for granted that application-specific memory allocators were superior to general purpose libraries. Berger, Zorn and McKinley’s paper demonstrated through a rigorous empirical study that this assumption is not well-founded, and gave insights into the reasons why general purpose allocators can outperform handcrafted ones. The paper also stands out for the quality of its empirical methodology.

I am grateful to OOPSLA not only for the check for $333.33 :), but also for giving me the chance to publicly stand up and thank my wonderful co-authors: my excellent colleague Ben Zorn and my awesome advisor, Kathryn McKinley (both now at Microsoft Research). The original paper actually did a bit more than the citation – here’s the abstract from the original paper.

Programmers hoping to achieve performance improvements often use custom memory allocators. This in-depth study examines eight applications that use custom allocators. Surprisingly, for six of these applications, a state-of-the-art general-purpose allocator (the Lea allocator) performs as well as or better than the custom allocators. The two exceptions use regions, which deliver higher performance (improvements of up to 44%). Regions also reduce programmer burden and eliminate a source of memory leaks. However, we show that the inability of programmers to free individual objects within regions can lead to a substantial increase in memory consumption. Worse, this limitation precludes the use of regions for common programming idioms, reducing their usefulness.

We present a generalization of general-purpose and region-based allocators that we call reaps. Reaps are a combination of regions and heaps, providing a full range of region semantics with the addition of individual object deletion. We show that our implementation of reaps provides high performance, outperforming other allocators with region-like semantics. We then use a case study to demonstrate the space advantages and software engineering benefits of reaps in practice. Our results indicate that programmers needing fast regions should use reaps, and that most programmers considering custom allocators should instead use the Lea allocator.

Slight correction: they should instead use Hoard :).