[Spark-11968][ML][MLLIB]Optimize MLLIB ALS recommendForAll

mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala

@@ -39,6 +39,7 @@ import org.apache.spark.mllib.util.{Loader, Saveable}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Row, SparkSession}
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.BoundedPriorityQueue
 
 /**
  * Model representing the result of matrix factorization.
@@ -277,17 +278,39 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
     val srcBlocks = blockify(rank, srcFeatures)
     val dstBlocks = blockify(rank, dstFeatures)
     val ratings = srcBlocks.cartesian(dstBlocks).flatMap {
-      case ((srcIds, srcFactors), (dstIds, dstFactors)) =>
-        val m = srcIds.length
-        val n = dstIds.length
-        val ratings = srcFactors.transpose.multiply(dstFactors)
-        val output = new Array[(Int, (Int, Double))](m * n)
-        var k = 0
-        ratings.foreachActive { (i, j, r) =>
-          output(k) = (srcIds(i), (dstIds(j), r))
-          k += 1
-        }
-        output.toSeq
+      case (users, items) =>
+      val m = users.size
+      val n = math.min(items.size, num)
+      val output = new Array[(Int, (Int, Double))](m * n)
+      var j = 0
+      users.foreach (user => {
+          def order(a: (Int, Double)) = a._2
+          val pq: BoundedPriorityQueue[(Int, Double)] =
+            new BoundedPriorityQueue[(Int, Double)](n)(Ordering.by(order))
+          items.foreach (item => {
+              /**
+               * blas.ddot (F2jBLAS) is the same performance with the following code.
+               * the performace of blas.ddot with NativeBLAS is very bad.
+               * blas.ddot (F2jBLAS) is about 10% improvement comparing with linalg.dot.
+               * val rate = blas.ddot(rank, user._2, 1, item._2, 1)
+               */
+              var rate: Double = 0
+              var k = 0
+              while(k < rank) {
+                rate += user._2(k) * item._2(k)
+                k += 1
+              }
+              pq += ((item._1, rate))
+            })
+          val pqIter = pq.iterator
+          var i = 0
+          while(i < n) {
+            output(j + i) = (user._1, pqIter.next())
+            i += 1
+          }
+          j += n
+      })
+      output.toSeq
     }
     ratings.topByKey(num)(Ordering.by(_._2))
   }
@@ -297,23 +320,10 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
    */
   private def blockify(
       rank: Int,
-      features: RDD[(Int, Array[Double])]): RDD[(Array[Int], DenseMatrix)] = {
+      features: RDD[(Int, Array[Double])]): RDD[Seq[(Int, Array[Double])]] = {
     val blockSize = 4096 // TODO: tune the block size
-    val blockStorage = rank * blockSize
     features.mapPartitions { iter =>
-      iter.grouped(blockSize).map { grouped =>
-        val ids = mutable.ArrayBuilder.make[Int]
-        ids.sizeHint(blockSize)
-        val factors = mutable.ArrayBuilder.make[Double]
-        factors.sizeHint(blockStorage)
-        var i = 0
-        grouped.foreach { case (id, factor) =>
-          ids += id
-          factors ++= factor
-          i += 1
-        }
-        (ids.result(), new DenseMatrix(rank, i, factors.result()))
-      }
+      iter.grouped(blockSize)
     }
   }