Toward Efficient Architecture-Independent Algorithms for Dynamic Programs

TLDR: It is shown that a class of grid-based parallel recursive divide-and-conquer algorithms can be run with provably optimal or near-optimal performance bounds on fat cores (cache complexity), thin cores (data movements), and purely distributed-memory machines (communication complexity) without changing the algorithm’s basic structure.

Abstract: We argue that the recursive divide-and-conquer paradigm is highly suited for designing algorithms to run efficiently under both shared-memory (multi- and manycores) and distributed-memory settings. The depth-first recursive decomposition of tasks and data is known to allow computations with potentially high temporal locality, and automatic adaptivity when resource availability (e.g., available space in shared caches) changes during runtime. Higher data locality leads to better intra-node I/O and cache performance and lower inter-node communication complexity, which in turn can reduce running times and energy consumption. Indeed, we show that a class of grid-based parallel recursive divide-and-conquer algorithms (for dynamic programs) can be run with provably optimal or near-optimal performance bounds on fat cores (cache complexity), thin cores (data movements), and purely distributed-memory machines (communication complexity) without changing the algorithm’s basic structure.