ProfDP

Abstract

New memory technologies, such as non-volatile memory and stacked memory, have reformed the memory hierarchies in modern and emerging computer architectures. It becomes common to see memories of different types integrated into the same system, as known as heterogeneous memory. Typically, a heterogeneous memory system consists of a small fast component and a large slow component. This encourages new style of data processing and exposes developers with a new problem: given two memory types, how shall we redesign applications to benefit from this memory arrangement and decide on the efficient data placement? Existing methods perform detailed memory access pattern analysis to guide data placement. However, these methods are heavyweight and ignore the interactions between software and hardware. To address these issues, we develop ProfDP, a lightweight profiler that employs differential data-centric analysis to provide intuitive guidance for data placement in heterogeneous memory. Evaluated with a number of parallel benchmarks running on a state-of-the-art emulator and a real machine with heterogeneous memory, we show that ProfDP is able to guide nearly-optimal data placement to maximize performance with minimum programming efforts.