zhengruifeng commented on pull request #28349:
URL: https://github.com/apache/spark/pull/28349#issuecomment-619692445
I also test on sparse dataset:
```
import org.apache.spark.ml.classification._
import org.apache.spark.storage.StorageLevel
val df = spark.read.option("numFeatures",
"8289919").format("libsvm").load("/data1/Datasets/webspam/webspam_wc_normalized_trigram.svm.10k").withColumn("label",
(col("label")+1)/2)
val svc = new LinearSVC().setMaxIter(10)
svc.fit(df)
val start = System.currentTimeMillis; val model1 =
svc.setMaxIter(30).fit(df); val end = System.currentTimeMillis; end - start
```
results:
this PR:
```
scala> val start = System.currentTimeMillis; val model1 =
svc.setMaxIter(30).fit(df); val end = System.currentTimeMillis; end - start
start: Long = 1587957534286
model1: org.apache.spark.ml.classification.LinearSVCModel = LinearSVCModel:
uid=linearsvc_2fcd0abbb2d7, numClasses=2, numFeatures=8289919
end: Long = 1587957684508
res1: Long = 150222
```
Master:
```
scala> val start = System.currentTimeMillis; val model1 =
svc.setMaxIter(30).fit(df); val end = System.currentTimeMillis; end - start
start: Long = 1587957959670
model1: org.apache.spark.ml.classification.LinearSVCModel = LinearSVCModel:
uid=linearsvc_269e4f373d2c, numClasses=2, numFeatures=8289919
end: Long = 1587958111562
res1: Long = 151892
```
If we keep `blockSIze=1`, then there is no performance regression on sparse
dataset.
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