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https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14521944#comment-14521944
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Ilya Ganelin commented on SPARK-7075:
-------------------------------------
This looks like the result of a large internal Databricks effort - are there
pieces of this where you could use external help or is this issue in place
primarily to document migration of internal code?
> Project Tungsten: Improving Physical Execution and Memory Management
> --------------------------------------------------------------------
>
> Key: SPARK-7075
> URL: https://issues.apache.org/jira/browse/SPARK-7075
> Project: Spark
> Issue Type: Epic
> Components: Block Manager, Shuffle, Spark Core, SQL
> Reporter: Reynold Xin
> Assignee: Reynold Xin
>
> Based on our observation, majority of Spark workloads are not bottlenecked by
> I/O or network, but rather CPU and memory. This project focuses on 3 areas to
> improve the efficiency of memory and CPU for Spark applications, to push
> performance closer to the limits of the underlying hardware.
> 1. Memory Management and Binary Processing: leveraging application semantics
> to manage memory explicitly and eliminate the overhead of JVM object model
> and garbage collection
> 2. Cache-aware computation: algorithms and data structures to exploit memory
> hierarchy
> 3. Code generation: using code generation to exploit modern compilers and CPUs
> Several parts of project Tungsten leverage the DataFrame model, which gives
> us more semantics about the application. We will also retrofit the
> improvements onto Spark’s RDD API whenever possible.
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