Class/Object

io.fsq.hfile.reader.concrete.mmap

CacheSim

Related Docs: object CacheSim | package mmap

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abstract class CacheSim[Key] extends AnyRef

Implements a simulation of lru cache behavior based on Mattson's classic paper[1] It gives us an idea of what the cache hit rate would be for any cache size. It's roughly O(number of blocks) per lookup and so is not suited for high qps collections. If required [2] lets you simulate m different cache sizes in O(m) time per lookup. 1. "Evaluation techniques for storage hierarchies" http://www-inst.eecs.berkeley.edu/~cs266/sp10/readings/mattson70.pdf 2. "Implementing Stack Simulation for Highly-Associative Memories" http://pages.cs.wisc.edu/~markhill/papers/sigmetrics91_fully_assoc.pdf

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Instance Constructors

  1. new CacheSim(numKeys: Long, pool: FuturePool = CacheSim.pool)

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Abstract Value Members

  1. abstract def accessInternal(k: Key): Unit

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Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def access(k: Key): Unit

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  5. var accessCount: Double

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. def cumlativeHist: ListMap[String, Double]

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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. val histogram: Map[Long, Long]

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  15. final def isInstanceOf[T0]: Boolean

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. final def synchronized[T0](arg0: ⇒ T0): T0

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  20. def toString(): String

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  21. final def wait(): Unit

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  22. final def wait(arg0: Long, arg1: Int): Unit

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  23. final def wait(arg0: Long): Unit

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