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- W2913778848 abstract "Thread synchronization using shared memory hardware cache coherence paradigm is prevalent in multicore processors. However, as the number of cores increase on a chip, cache line ping-pong prevents performance scaling for algorithms that deploy fine-grain synchronization. This article proposes an in-hardware moving computation to data model (MC) that pins shared data at dedicated cores. The critical code sections are serialized and executed at these cores in a spatial setting to enable data locality optimizations. In-hardware messages enable non-blocking and blocking communication between cores, without involving the cache coherence protocol. The in-hardware MC model is implemented on Tilera Tile-Gx72 multicore platform to evaluate 8- to 64-core count scale. A simulated RISC-V multicore environment is built to further evaluate the performance scaling advantages of the MC model at 1,024-cores scale. The evaluation using graph and machine-learning benchmarks illustrates that atomic instructions based synchronization scales up to 512 cores, and the MC model at the same core count outperforms by 27% in completion time and 39% in dynamic energy consumption." @default.
- W2913778848 created "2019-02-21" @default.
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- W2913778848 date "2019-02-13" @default.
- W2913778848 modified "2023-10-16" @default.
- W2913778848 title "Accelerating Synchronization Using Moving Compute to Data Model at 1,000-core Multicore Scale" @default.
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- W2913778848 doi "https://doi.org/10.1145/3300208" @default.
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