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wapentake    
n. 小邑

小邑

Wapentake \Wap"en*take\ (?; 277), n. [AS. w?penge??c,
w?pent[=a]c, from Icel. v[=a]pnat[=a]k, literally, a weapon
taking or weapon touching, hence an expression of assent ("si
displicuit sententia fremitu aspernantur; sin placuit frameas
concutiunt." --Tacitus, "Germania," xi.). See {Weapon}, and
{Take}. This name had its origin in a custom of touching
lances or spears when the hundreder, or chief, entered on his
office. "Cum quis accipiebat praefecturam wapentachii, die
statuto in loco ubi consueverant congregari, omnes majores
natu contra eum conveniebant, et descendente eo de equo suo,
omnes assurgebant ei. Ipse vero, erecta lancea sua, ab
omnibus secundum morem f[oe]dus accipiebat; omnes enim
quot-quot venissent cum lanceis suis ipsius hastam tangebant,
et ita se confirmabant per contactum armorum, pace palam
concessa. Waepnu enim arma sonat; tac, tactus est -- hac de
causa totus ille conventus dicitur Wapentac, eo quod per
tactum armorum suorum ad invicem conf[oe]derati sunt." --L L.
Edward Confessor, 33. D. Wilkins.]
In some northern counties of England, a division, or
district, answering to the hundred in other counties.
Yorkshire, Lincolnshire, and Nottinghamshire are divided into
wapentakes, instead of hundreds. [Written also {wapentac}.]
--Selden. Blackstone.
[1913 Webster]


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  • GitHub - apache gluten: Gluten is a middle layer responsible for . . .
    Gluten's target users include anyone who wants to fundamentally accelerate Spark SQL As a plugin to Spark, Gluten requires no changes to the DataFrame API or SQL queries; users only need to configure it correctly
  • gluten gluten-core src main java org apache gluten memory memtarget . . .
    Use of System gc () is generally discouraged due to its unpredictable performance impact, but here it is used as a last resort to prevent memory allocation failures
  • VeloxDynamicSizingOffheap | Apache Gluten
    Gluten requires setting both on-heap and off-heap memory sizes, which initializes different memory layouts Improper configuration of these settings can lead to lower performance To fix this issue, dynamic off-heap sizing is an experimental feature designed to simplify this process
  • VeloxDynamicSizingOffheap | gluten - apache. github. io
    Gluten requires setting both on-heap and off-heap memory sizes, which initializes different memory layouts Improper configuration of these settings can lead to lower performance To fix this issue, dynamic off-heap sizing is an experimental feature designed to simplify this process
  • Home | Apache Gluten
    Gluten’s target user is anyone who wants to accelerate SparkSQL fundamentally As a plugin to Spark, Gluten doesn’t require any change for dataframe API or SQL query, but only requires user to make correct configuration
  • Apache Gluten (Incubating): A Middle Layer for Offloading JVM-based SQL . . .
    In Gluten, the native backend requires more off-heap memory, so we recommend configuring a larger spark memory offHeap size to improve performance and reduce the risk of OOM errors
  • Memory Management | apache incubator-gluten | DeepWiki
    This document covers the memory management system within the Velox backend of Apache Gluten It details how native memory allocation is coordinated between the JVM (Spark) and native execution engines
  • Spark Integration | apache gluten | DeepWiki
    Gluten integrates with Apache Spark through Spark's plugin API (org apache spark api plugin SparkPlugin) The main entry point is GlutenPlugin, which registers backend implementations and injects columnar rules into Spark's query planning pipeline The backend is selected through configuration
  • Apache Gluten accelerated Spark engine - IBM
    Apache Gluten requires Large Off Heap memory as it integrates with native backend that is, Velox using Apache Arrow's OffHeap columnar format, which is essential for large-scale data processing without exceeding JVM heap limits
  • Apache Gluten: Supercharging Spark with Native C++ Execution
    For teams running Spark on Databricks, Gluten is interesting mostly as a comparison point to Photon — and a possible exit strategy if you want similar native-execution speedups without the Databricks dependency





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