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From "Chris Goffinet (JIRA)" <j...@apache.org>
Subject [jira] Updated: (CASSANDRA-1632) Thread workflow and cpu affinity
Date Tue, 19 Oct 2010 20:11:45 GMT

     [ https://issues.apache.org/jira/browse/CASSANDRA-1632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chris Goffinet updated CASSANDRA-1632:
--------------------------------------

    Description: 
Here are some thoughts I wanted to write down, we need to run some serious benchmarks to see
the benefits:

1) All thread pools for our stages use a shared queue per stage. For some stages we could
move to a model where each thread has its own queue. This would reduce lock contention on
the shared queue. This workload only suits the stages that have no variance, else you run
into thread starvation. Some stages that this might work: ROW-MUTATION.

2) Set cpu affinity for each thread in each stage. If we can pin threads to specific cores,
and control the workflow of a message from Thrift down to each stage, we should see improvements
on reducing L1 cache misses. We would need to build a JNI extension (to set cpu affinity),
as I could not find anywhere in JDK where it was exposed. 

3) Batching the delivery of requests across stage boundaries. Peter Schuller hasn't looked
deep enough yet into the JDK, but he thinks there may be significant improvements to be had
there. Especially in high-throughput situations. If on each consumption you were to consume
everything in the queue, rather than implying a synchronization point in between each request.


  was:
Here are some thoughts I wanted to write down, we need to run some serious benchmarks to see
the benefits:

1) All thread pools for our stages use a shared queue per stage. For some stages we could
move to a model where each thread has its own queue. This would reduce lock contention on
the shared queue. This workload only suits the stages that have no variance, else you run
into thread starvation. Some stages that this might work: ROW-MUTATION.

2) Set cpu affinity for each thread in each stage. If we can pin threads to specific cores,
and control the workflow of a message from Thrift down to each stage, we should see improvements
on reducing L1 cache misses. We would need to build a JNI extension, as I could not find anywhere
in JDK where it was exposed. 

3) Batching the delivery of requests across stage boundaries. Peter Schuller hasn't looked
deep enough yet into the JDK, but he thinks there may be significant improvements to be had
there. Especially in high-throughput situations. If on each consumption you were to consume
everything in the queue, rather than implying a synchronization point in between each request.



> Thread workflow and cpu affinity
> --------------------------------
>
>                 Key: CASSANDRA-1632
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-1632
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Chris Goffinet
>             Fix For: 0.7.1
>
>
> Here are some thoughts I wanted to write down, we need to run some serious benchmarks
to see the benefits:
> 1) All thread pools for our stages use a shared queue per stage. For some stages we could
move to a model where each thread has its own queue. This would reduce lock contention on
the shared queue. This workload only suits the stages that have no variance, else you run
into thread starvation. Some stages that this might work: ROW-MUTATION.
> 2) Set cpu affinity for each thread in each stage. If we can pin threads to specific
cores, and control the workflow of a message from Thrift down to each stage, we should see
improvements on reducing L1 cache misses. We would need to build a JNI extension (to set cpu
affinity), as I could not find anywhere in JDK where it was exposed. 
> 3) Batching the delivery of requests across stage boundaries. Peter Schuller hasn't looked
deep enough yet into the JDK, but he thinks there may be significant improvements to be had
there. Especially in high-throughput situations. If on each consumption you were to consume
everything in the queue, rather than implying a synchronization point in between each request.

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