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• W4200525217 abstract "One widely used metric that measures data locality is reuse distance —the number of unique memory locations that are accessed between two consecutive accesses to a particular memory location. State-of-the-art techniques that measure reuse distance in parallel applications rely on simulators or binary instrumentation tools that incur large performance and memory overheads. Moreover, the existing sampling-based tools are limited to measuring reuse distances of a single thread and discard interactions among threads in multi-threaded programs. In this work, we propose ReuseTracker —a fast and accurate reuse distance analyzer that leverages existing hardware features in commodity CPUs. ReuseTracker is designed for multi-threaded programs and takes cache-coherence effects into account. By utilizing hardware features like performance monitoring units and debug registers, ReuseTracker can accurately profile reuse distance in parallel applications with much lower overheads than existing tools. It introduces only 2.9× runtime and 2.8× memory overheads. Our tool achieves 92% accuracy when verified against a newly developed configurable benchmark that can generate a variety of different reuse distance patterns. We demonstrate the tool’s functionality with two use-case scenarios using PARSEC, Rodinia, and Synchrobench benchmark suites where ReuseTracker guides code refactoring in these benchmarks by detecting spatial reuses in shared caches that are also false sharing and successfully predicts whether some benchmarks in these suites can benefit from adjacent cache line prefetch optimization." @default.
• W4200525217 created "2021-12-31" @default.
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• W4200525217 date "2021-12-06" @default.
• W4200525217 modified "2023-10-16" @default.
• W4200525217 title "<scp>ReuseTracker</scp> : Fast Yet Accurate Multicore Reuse Distance Analyzer" @default.
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• W4200525217 doi "https://doi.org/10.1145/3484199" @default.
• W4200525217 hasPublicationYear "2021" @default.
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