Optimizing Differential Computation for Large-Scale Graph Processing

Differential computation (DC) has emerged as a powerful general technique for maintaining computations over evolving datasets, even those containing arbitrarily nested loops, making DC particularly well-suited for graph computations. However, the general maintenance technique used by DC makes it less efficient for application-specific workloads. This paper shows how application-specific optimizations can improve both the runtime and memory characteristics of Differential Dataflow (DD), the reference implementation of DC. We present three optimizations for DD that make it more suitable for graph processing. Our first two optimizations are a redesign the in-memory indices that DD uses to maintain operator state, making them more read-friendly and decreasing the amount of data that operators need to scan from the indices. Next, we observe that DD's Reduce operator performs an expensive recomputation to determine whether or not there are any new outputs for changed inputs, even if the outputs have not actually changed. Our third optimization, called Fast Empty Difference Verification, detects when there are no output changes without performing DD's default rerunning logic. We present experiments on a variety of graph computation workloads demonstrating that our optimizations improve DD's runtime by up to 19× and reduce memory consumption by up to 1.7×.