[jira] [Updated] (SPARK-17033) GaussianMixture should use treeAggregate to improve performance
[
https://issues.apache.org/jira/browse/SPARK-17033?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yanbo Liang updated SPARK-17033:
Description: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there are 20% increased performance.
(was: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there are 15% increased performance.)
> GaussianMixture should use treeAggregate to improve performance
> ---
>
> Key: SPARK-17033
> URL: https://issues.apache.org/jira/browse/SPARK-17033
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
>Reporter: Yanbo Liang
>Priority: Minor
>
> {{GaussianMixture}} should use {{treeAggregate}} rather than {{aggregate}} to
> improve performance and scalability. In my test of dataset with 200 features
> and 1M instance, I found there are 20% increased performance.
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[jira] [Updated] (SPARK-17033) GaussianMixture should use treeAggregate to improve performance
[
https://issues.apache.org/jira/browse/SPARK-17033?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yanbo Liang updated SPARK-17033:
Description: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there are 15% increased performance.
(was: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there are 20% increased performance.)
> GaussianMixture should use treeAggregate to improve performance
> ---
>
> Key: SPARK-17033
> URL: https://issues.apache.org/jira/browse/SPARK-17033
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
>Reporter: Yanbo Liang
>Priority: Minor
>
> {{GaussianMixture}} should use {{treeAggregate}} rather than {{aggregate}} to
> improve performance and scalability. In my test of dataset with 200 features
> and 1M instance, I found there are 15% increased performance.
--
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[jira] [Updated] (SPARK-17033) GaussianMixture should use treeAggregate to improve performance
[
https://issues.apache.org/jira/browse/SPARK-17033?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yanbo Liang updated SPARK-17033:
Description: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there is 20% increased performance.
(was: {{GaussianMixture}} should use {{treeAggregate}} rather than
{{aggregate}} to improve performance and scalability. In my test of dataset
with 200 features and 1M instance, I found there are 20% increased performance.)
> GaussianMixture should use treeAggregate to improve performance
> ---
>
> Key: SPARK-17033
> URL: https://issues.apache.org/jira/browse/SPARK-17033
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
>Reporter: Yanbo Liang
>Priority: Minor
>
> {{GaussianMixture}} should use {{treeAggregate}} rather than {{aggregate}} to
> improve performance and scalability. In my test of dataset with 200 features
> and 1M instance, I found there is 20% increased performance.
--
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[jira] [Updated] (SPARK-17033) GaussianMixture should use treeAggregate to improve performance
[
https://issues.apache.org/jira/browse/SPARK-17033?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yanbo Liang updated SPARK-17033:
Component/s: MLlib
ML
> GaussianMixture should use treeAggregate to improve performance
> ---
>
> Key: SPARK-17033
> URL: https://issues.apache.org/jira/browse/SPARK-17033
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
>Reporter: Yanbo Liang
>Priority: Minor
>
> {{GaussianMixture}} should use {{treeAggregate}} rather than {{aggregate}} to
> improve performance and scalability. In my test of dataset with 200 features
> and 1M instance, I found there are 20% increased performance.
--
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