Two-dimensional rectangular bin packing (2DBP) is a known abstraction of dynamic storage allocation (DSA). We argue that such abstractions can aid practical purposes. 2DBP algorithms optimize their placements' makespan, i.e., the size of the used address range. At first glance modern virtual memory systems with demand paging render makespan irrelevant as an optimization criterion: allocators commonly employ sparse addressing and need worry only about fragmentation caused within page boundaries. But in the embedded domain, where portions of memory are statically pre-allocated, makespan remains a reasonable metric.

Recent work has shown that viewing allocators as black-box 2DBP solvers bears meaning. For instance, there exists a 2DBP-based fragmentation metric which often correlates monotonically with maximum resident set size (RSS). Given the field's indeterminacy with respect to fragmentation definitions, as well as the immense value of physical memory savings, we are motivated to set allocator-generated placements against their 2DBP-devised, makespan-optimizing counterparts. Of course, allocators must operate online while 2DBP algorithms work on complete request traces; but since both sides optimize criteria related to minimizing memory wastage, the idea of studying their relationship preserves its intellectual–and practical–interest.

Unfortunately no implementations of 2DBP algorithms for DSA are available. This paper presents a first, though partial, implementation of the state-of-the-art. We validate its functionality by comparing its outputs' makespan to the theoretical upper bound provided by the original authors. Along the way, we identify and document key details to assist analogous future efforts.

Our experiments comprise 4 modern allocators and 8 real application workloads. We make several notable observations on our empirical evidence: in terms of makespan, allocators outperform Robson's worst-case lower bound $93.75%$ of the time. In $87.5%$ of cases, GNU's \texttt{malloc} implementation demonstrates equivalent or superior performance to the 2DBP state-of-the-art, despite the second operating offline.

Most surprisingly, the 2DBP algorithm proves competent in terms of fragmentation, producing up to $2.46$x better solutions. Future research can leverage such insights towards memory-targeting optimizations.