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From "Reynold Xin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-7075) Project Tungsten: Improving Physical Execution and Memory Management
Date Wed, 29 Apr 2015 23:21:06 GMT

    [ https://issues.apache.org/jira/browse/SPARK-7075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14520499#comment-14520499
] 

Reynold Xin commented on SPARK-7075:
------------------------------------

Yup I will post more thoughts and plans in the next few days.


> 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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