[SPARK-21688] performance improvement in mllib SVM with native BLAS

• Type: Improvement
• Status: Resolved
• Priority: Minor
• Resolution: Won't Fix
• Affects Version/s: 2.2.0
• Component/s: MLlib

Environment

4 nodes: 1 master node, 3 worker nodes
model name : Intel(R) Xeon(R) CPU E5-2697 v2 @ 2.70GHz
Memory : 180G
num of core per node: 10

• Assignee: Unassigned
• Reporter: Vincent
• Created: 10/Aug/17 05:40
• Updated: 28/Dec/18 16:07
• Resolved: 28/Dec/18 16:07

Description

in current mllib SVM implementation, we found that the CPU is not fully utilized, one reason is that f2j blas is set to be used in the HingeGradient computation. As we found out earlier (https://issues.apache.org/jira/browse/SPARK-21305) that with proper settings, native blas is generally better than f2j on the uni-test level, here we make the blas operations in SVM go with MKL blas and get an end to end performance report showing that in most cases native blas outperformance f2j blas up to 50%.
So, we suggest removing those f2j-fixed calling and going for native blas if available. If this proposal is acceptable, we will move on to benchmark other algorithms impacted.

Issue Links

• relates to: SPARK-21305 The BKM (best known methods) of using native BLAS to improvement ML/MLLIB performance
• links to: [Github] Pull Request #18936 (VinceShieh)
• links to: GitHub Pull Request #18936