Traceable data types for self-adjusting computation

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 (e.g., 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 granof the query and update operations of arbitrary (abstract) data types, instead of just reads and writes on memory cells. This can significantly reduce the numbe...

• Type: Conference
• Year: 2010
• Where: PLDI
• Authors: Umut A. Acar, Guy E. Blelloch, Ruy Ley-Wild, Kanat Tangwongsan, Duru Türkoglu
• Keywords: Data Types, PLDI 2010, Self-adjusting Computation, Self-adjusting Computation Guarantee