srowen commented on a change in pull request #30810:
URL: https://github.com/apache/spark/pull/30810#discussion_r547426492
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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
- // For level-1 function dspmv, use f2jBLAS for better performance.
- private[ml] def f2jBLAS: NetlibBLAS = {
- if (_f2jBLAS == null) {
- _f2jBLAS = new F2jBLAS
+ // For level-1 function dspmv, use javaBLAS for better performance.
+ private[ml] def javaBLAS: NetlibBLAS = {
+ if (_javaBLAS == null) {
+ _javaBLAS =
+ try {
+ // scalastyle:off classforname
+ Class.forName("org.apache.spark.ml.linalg.VectorizedBLAS", true,
+ Option(Thread.currentThread().getContextClassLoader)
+ .getOrElse(getClass.getClassLoader))
+ .newInstance()
+ .asInstanceOf[NetlibBLAS]
+ // scalastyle:on classforname
+ } catch {
+ case _: Throwable => new F2jBLAS
+ }
+ }
+ _javaBLAS
+ }
+
+ // For level-3 routines, we use the native BLAS.
+ private[ml] def nativeBLAS: NetlibBLAS = {
+ if (_nativeBLAS == null) {
+ _nativeBLAS =
+ if (NetlibBLAS.getInstance.isInstanceOf[F2jBLAS]) {
+ javaBLAS
+ } else {
+ NetlibBLAS.getInstance
+ }
}
- _f2jBLAS
+ _nativeBLAS
}
private[ml] def getBLAS(vectorSize: Int): NetlibBLAS = {
if (vectorSize < nativeL1Threshold) {
- f2jBLAS
+ javaBLAS
Review comment:
OK I get that. If the Vector API is available, then, no real reason to
use the plain Java implementation ever. This makes sense to me. You can still
use Netlib + BLAS by providing it if desired.
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