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This work considers dynamic memory management for population-basedprobabilistic programs, such as those using particle methods for inference.Such programs exhibit a pattern of allocating, copying, potentially mutating,and deallocating collections of similar objects through successive generations.These objects may assemble data structures such as stacks, queues, lists,ragged arrays, and trees, which may be of random, and possibly unbounded, size.For the simple case of $N$ particles, $T$ generations, $D$ objects, andresampling at each generation, dense representation requires $O(DNT)$ memory,while sparse representation requires only $O(DT+DN\log DN)$ memory, based onexisting theoretical results. This work describes an object copy-on-writeplatform to automate this saving for the programmer. The core idea isformalized using labeled directed multigraphs, where vertices representobjects, edges the pointers between them, and labels the necessary bookkeeping.A specific labeling scheme is proposed for high performance under themotivating pattern. The platform is implemented for the Birch probabilisticprogramming language, using smart pointers, hash tables, and reference-countinggarbage collection. It is tested empirically on a number of realisticprobabilistic programs, and shown to significantly reduce memory use andexecution time in a manner consistent with theoretical expectations. Thisenables copy-on-write for the imperative programmer, lazy deep copies for theobject-oriented programmer, and in-place write optimizations for the functionalprogrammer.

Lazy object copy as a platform for population-based probabilistic programming