[ https://issues.apache.org/jira/browse/SPARK-20446?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nick Pentreath closed SPARK-20446. ---------------------------------- Resolution: Duplicate > Optimize the process of MLLIB ALS recommendForAll > ------------------------------------------------- > > Key: SPARK-20446 > URL: https://issues.apache.org/jira/browse/SPARK-20446 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib > Affects Versions: 2.3.0 > Reporter: Peng Meng > > The recommendForAll of MLLIB ALS is very slow. > GC is a key problem of the current method. > The task use the following code to keep temp result: > val output = new Array[(Int, (Int, Double))](m*n) > m = n = 4096 (default value, no method to set) > so output is about 4k * 4k * (4 + 4 + 8) = 256M. This is a large memory and > cause serious GC problem, and it is frequently OOM. > Actually, we don't need to save all the temp result. Suppose we recommend > topK (topK is about 10, or 20) product for each user, we only need 4k * topK > * (4 + 4 + 8) memory to save the temp result. > I have written a solution for this method with the following test result. > The Test Environment: > 3 workers: each work 10 core, each work 30G memory, each work 1 executor. > The Data: User 480,000, and Item 17,000 > BlockSize: 1024 2048 4096 8192 > Old method: 245s 332s 488s OOM > This solution: 121s 118s 117s 120s > -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org