fommil commented on a change in pull request #30810:
URL: https://github.com/apache/spark/pull/30810#discussion_r626335537
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File path: mllib-local/src/main/scala/org/apache/spark/ml/linalg/BLAS.scala
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@@ -18,28 +18,51 @@
package org.apache.spark.ml.linalg
import com.github.fommil.netlib.{BLAS => NetlibBLAS, F2jBLAS}
-import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS}
/**
* BLAS routines for MLlib's vectors and matrices.
*/
private[spark] object BLAS extends Serializable {
- @transient private var _f2jBLAS: NetlibBLAS = _
+ @transient private var _javaBLAS: NetlibBLAS = _
@transient private var _nativeBLAS: NetlibBLAS = _
private val nativeL1Threshold: Int = 256
Review comment:
"small speed up of native for the level-1 and level-2 routines." I think
you need to do some more analysis on this. Native can be 10x faster than JVM
for reasonable sized matrices. However, as shown in
https://github.com/fommil/matrix-toolkits-java the EJML and common-math project
are faster for matrices of 10x10 or smaller. If you want to heavily optimise
for those usecases, then swap to using EJML which is heavily optimised for that
usecase (not just "something on the JVM")
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