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Browsing by Author "Pearce, Roger"

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    AUTOPAGER: Auto-tuning Memory-Mapped I/O Parameters in Userspace
    Youssef, Karim; Shah, Niteya; Gokhale, Maya; Pearce, Roger; Feng, Wu-chun (IEEE, 2022)
    The 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.
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    Optimizing Performance and Storage of Memory-Mapped Persistent Data Structures
    Youssef, Karim; Raqibul Islam, Abdullah A.; Iwabuchi, Keita; Feng, Wu-chun; Pearce, Roger (IEEE, 2022-01-01)
    Persistent data structures represent a core component of high-performance data analytics. Multiple data processing systems persist data structures using memory-mapped files. Memory-mapped file I/O provides a productive and unified programming interface to different types of storage systems. However, it suffers from multiple limitations, including performance bottlenecks caused by system-wide configurations and a lack of support for efficient incremental versioning. There-fore, many such systems only support versioning via full-copy snapshots, resulting in poor performance and storage capacity bottlenecks. To address these limitations, we present Privateer 2.0, a virtual memory and storage interface that optimizes performance and storage capacity for versioned persistent data structures. Privateer 2.0 improves over the previous version by supporting userspace virtual memory management and block compression. We integrated Privateer 2.0 into Metall, a C++ persistent data structure allocator, and LMDB, a widely-used key-value store database. Privateer 2.0 yielded up to 7.5× speedup and up to 300× storage space reduction for Metall incremental snapshots and 1.25× speedup with 11.7× storage space reduction for LMDB incremental snapshots.