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• W2795035377 abstract "Cache partitioning is now available in commercial hardware. In theory, software can leverage cache partitioning to use the last-level cache better and improve performance. In practice, however, current systems implement way-partitioning, which offers a limited number of partitions and often hurts performance. These limitations squander the performance potential of smart cache management. We present KPart, a hybrid cache partitioning-sharing technique that sidesteps the limitations of way-partitioning and unlocks significant performance on current systems. KPart first groups applications into clusters, then partitions the cache among these clusters. To build clusters, KPart relies on a novel technique to estimate the performance loss an application suffers when sharing a partition. KPart automatically chooses the number of clusters, balancing the isolation benefits of way-partitioning with its potential performance impact. KPart uses detailed profiling information to make these decisions. This information can be gathered either offline, or online at low overhead using a novel profiling mechanism. We evaluate KPart in a real system and in simulation. KPart improves throughput by 24% on average (up to 79%) on an Intel Broadwell-D system, whereas prior per-application partitioning policies improve throughput by just 1.7% on average and hurt 30% of workloads. Simulation results show that KPart achieves most of the performance of more advanced partitioning techniques that are not yet available in hardware." @default.
• W2795035377 created "2018-04-06" @default.
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• W2795035377 date "2018-02-01" @default.
• W2795035377 modified "2023-10-14" @default.
• W2795035377 title "KPart: A Hybrid Cache Partitioning-Sharing Technique for Commodity Multicores" @default.
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• W2795035377 doi "https://doi.org/10.1109/hpca.2018.00019" @default.
• W2795035377 hasPublicationYear "2018" @default.
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