Parallel Algorithms for Asymmetric Read-Write Costs

Motivated by the significantly higher cost of writing than reading in emerging memory technologies, we consider parallel algorithm design under such asymmetric read-write costs, with the goal of reducing the number of writes while preserving work-efficiency and low span. We present a nested-parallel model of computation that combines (i) small per-task stack-allocated memories with symmetric read-write costs and (ii) an unbounded heap-allocated shared memory with asymmetric read-write costs, and show how the costs in the model map efficiently onto a more concrete machine model under a work-stealing scheduler. We use the new model to design reduced write, work-efficient, low span parallel algorithms for a number of fundamental problems such as reduce, list contraction, tree contraction, breadth-first search, ordered filter, and planar convex hull. For the latter two problems, our algorithms are output-sensitive in that the work and number of writes decrease with the output size. We also present a reduced write, low span minimum spanning tree algorithm that is nearly work-efficient (off by the inverse Ackermann function). Our algorithms reveal several interesting techniques for significantly reducing shared memory writes in parallel algorithms without asymptotically increasing the number of shared memory reads.