Memory pressure is inevitable as the size of working sets is rapidly growing while the capacity of dynamic random access memory (DRAM) is not. Meanwhile, storage devices have evolved so that their speed is comparable to the speed of DRAM while their capacity scales are comparable to that of hard disk drives (HDD). Thus, hierarchial memory systems configuring DRAM as the main memory and high-end storages as swap devices will be common.

Due to the unique characteristics of these modern storage devices, the swap target decision should be optimal. It is essential to know the exact data access patterns of workloads for such an optimal decision, although underlying systems cannot accurately estimate such complex and dynamic patterns. For this reason, memory systems allow programs to voluntarily hint their data access pattern. Nevertheless, it is exhausting for a human to manually figure out the patterns and embed optimal hints if the workloads are huge and complex.

This project introduces a compiler extension that automatically optimizes a program to voluntarily hint its dynamic data access patterns to the underlying swap system using a static/dynamic analysis based profiling result. To our best knowledge, this is the first profile-guided optimization (PGO) for modern swap devices. Our empirical evaluation of the scheme using realistic workloads shows consistent improvement in performance and swap device lifetime up to 2.65 times and 2.98 times, respectively.

Publications And Presentations

• SeongJae Park, Yunjae Lee, Moonsub Kim Heon Y. Yeom, Automating Context Based Access Pattern Hint Injection for System Performance and Swap Storage Durability. In 11th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), July 2019. Paper, Slides
• SeongJae Park, Yunjae Lee, Moonsub Kim, Heon Y. Yeom, Automated Data Access Pattern Hint Instrumentation for System Performance and Durability of Swap Storages. (WiP) In 17th USENIX Conference on File and Storage Technologies (FAST), February 2019. Link