Traceable data types for self-adjusting computation

TL;DR: A semantics for tracing is developed, an existing self-adjusting language, ΔML, and its implementation are extended to support traceable data types, and an experimental evaluation is presented by considering a number of benchmarks.

Abstract: Self-adjusting computation provides an evaluation model where computations can respond automatically to modifications to their data by using a mechanism for propagating modifications through the computation. Current approaches to self-adjusting computation guarantee correctness by recording dependencies in a trace at the granularity of individual memory operations. Tracing at the granularity of memory operations, however, has some limitations: it can be asymptotically inefficient (\eg, compared to optimal solutions) because it cannot take advantage of problem-specific structure, it requires keeping a large computation trace (often proportional to the runtime of the program on the current input), and it introduces moderately large constant factors in practice.In this paper, we extend dependence-tracing to work at the granularity of the query and update operations of arbitrary (abstract) data types, instead of just reads and writes on memory cells. This can significantly reduce the number of dependencies that need to be kept in the trace and followed during an update. We define an interface for supporting a traceable version of a data type, which reports the earliest query that depends on (is changed by) revising operations back in time, and implement several such structures, including priority queues, queues, dictionaries, and counters. We develop a semantics for tracing, extend an existing self-adjusting language, ΔML, and its implementation to support traceable data types, and present an experimental evaluation by considering a number of benchmarks. Our experiments show dramatic improvements on space and time, sometimes by as much as two orders of magnitude.