Youssef, KarimShah, NiteyaGokhale, MayaPearce, RogerFeng, Wu-chun2024-03-042024-03-04202297816654978622377-6943https://hdl.handle.net/10919/118257The exponential growth in dataset sizes has shifted the bottleneck of high-performance data analytics from the compute subsystem to the memory and storage subsystems. This bottleneck has led to the proliferation of non-volatile memory (NVM). To bridge the performance gap between the Linux I/O subsystem and NVM, userspace memory-mapped I/O enables application-specific I/O optimizations. Specifically, UMap, an open-source userspace memory-mapping tool, exposes tunable paging parameters to application users, such as page size and degree of paging concurrency. Tuning these parameters is computationally intractable due to the vast search space and the cost of evaluating each parameter combination. To address this challenge, we present Autopager, a tool for auto-tuning userspace paging parameters. Our evaluation, using five data-intensive applications with UMap, shows that Autopager automatically achieves comparable performance to exhaustive tuning with 10 x less tuning overhead. and 16.3 x and 1.52 x speedup over UMap with default parameters and UMap with page-size only tuning, respectively.7 page(s)application/pdfenIn Copyrightautotuningvirtual memorybig datapagingmemory-mapped I/OmemorystorageConference proceedinghttps://doi.org/10.1109/HPEC55821.2022.9926409Feng, Wu-chun [0000-0002-6015-0727]