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We study the black hole information problem within a semiclassicallygravitating AdS _d d black hole coupled to and in equilibrium with a d d -dimensionalthermal conformal bath. We deform the bath state by a relevant scalardeformation, triggering a holographic RG flow whose "trans-IR"region deforms from a Schwarzschild geometry to a Kasner universe. Thesetup manifests two independent scales which control both the extent ofcoarse-graining and the entanglement dynamics when counting Hawkingdegrees of freedom in the bath. In tuning either, we find nontrivialchanges to the Page time and Page curve. We consequently view the Pagecurve as a probe of the holographic RG flow, with a higher Page timemanifesting as a result of increased coarse-graining of the bath degreesof freedom.
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PageCmp: Bandwidth Efficient Page Deduplication through In-memory Page Comparison
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Regionalization techniques group spatial areas into a set of homogeneous regions to analyze and draw conclusions about spatial phenomena. A recent regionalization problem, called MP-regions, groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. The MP-regions problem is NP-hard. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost and inherently centralized approaches to process data. This article introduces a parallel scalable regionalization framework (PAGE) to support MP-regions on large datasets. The proposed framework works in two stages. The first stage finds an initial solution through randomized search, and the second stage improves this solution through efficient heuristic search. To build an initial solution efficiently, we extend traditional spatial partitioning techniques to enable parallelized region building without violating the spatial constraints. Furthermore, we optimize the region building efficiency and quality by tuning the randomized area selection to trade off runtime with region homogeneity. The experimental evaluation shows the superiority of our framework to support an order of magnitude larger datasets efficiently compared to the state-of-the-art techniques while producing high-quality solutions.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISVLSI.2019.00023
