Iterative Data-parallel Mark&Sweep on a GPU

摘要

Automatic memory management makes programming easier. This is also true for general purpose GPU computing where currently no garbage collectors exist. In this paper we present a parallel mark-and-sweep collector to collect GPU memory on the GPU and tune its performance. Performance is increased by: (1) dataparallel marking and sweeping of regions of memory, (2) marking all elements of large arrays in parallel, (3) trading recursion over parallelism to match deeply linked data structures.