Bounding the effect of partition camping in GPU kernels

Abstract

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge and complement these performance models and tools, by modeling and analyzing a lesser known, but very severe performance pitfall, called Partition Camping, in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of GPU kernels by up to seven-fold. There is no existing tool that can detect the partition camping effect in GPU kernels.