{"action":"opened","number":5909,"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/5909","id":34717944,"html_url":"https://github.com/apache/spark/pull/5909","diff_url":"https://github.com/apache/spark/pull/5909.diff","patch_url":"https://github.com/apache/spark/pull/5909.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/5909","number":5909,"state":"open","locked":false,"title":"[SPARK-7368][MLlib] Add QR decomposition for RowMatrix","user":{"login":"hhbyyh","id":7981698,"avatar_url":"https://avatars.githubusercontent.com/u/7981698?v=3","gravatar_id":"","url":"https://api.github.com/users/hhbyyh","html_url":"https://github.com/hhbyyh","followers_url":"https://api.github.com/users/hhbyyh/followers","following_url":"https://api.github.com/users/hhbyyh/following{/other_user}","gists_url":"https://api.github.com/users/hhbyyh/gists{/gist_id}","starred_url":"https://api.github.com/users/hhbyyh/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hhbyyh/subscriptions","organizations_url":"https://api.github.com/users/hhbyyh/orgs","repos_url":"https://api.github.com/users/hhbyyh/repos","events_url":"https://api.github.com/users/hhbyyh/events{/privacy}","received_events_url":"https://api.github.com/users/hhbyyh/received_events","type":"User","site_admin":false},"body":"jira: https://issues.apache.org/jira/browse/SPARK-7368 \r\nAdd QR decomposition for RowMatrix. \r\n\r\nI'm not sure what's the blueprint about the distributed Matrix from community and whether this will be a desirable feature , so I sent a prototype for discussion. I'll go on polish the code and provide ut and performance statistics if it's acceptable.\r\n\r\nThe implementation refers to the [paper: https://www.cs.purdue.edu/homes/dgleich/publications/Constantine%202011%20-%20TSQR.pdf]\r\nAustin R. Benson, David F. Gleich, James Demmel. \"Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures\", 2013 IEEE International Conference on Big Data,\r\n\r\nwhich is a stable algorithm with good scalability.\r\n\r\nCurrently I tried it on a 400000 * 500 rowMatrix and it can bring down the computation time from 8.8 mins (using breeze.linalg.qr.reduced)  to 2.6 mins on a 4 worker cluster. I think there will still be some root for performance improvement.\r\n\r\nAny trial and suggestion is 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