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From MLnick <...@git.apache.org>
Subject [GitHub] spark pull request #17742: [Spark-11968][ML][MLLIB]Optimize MLLIB ALS recomm...
Date Wed, 26 Apr 2017 11:37:10 GMT
Github user MLnick commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17742#discussion_r113428600
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
    @@ -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())
    --- End diff --
    
    And here `srcFactor` instead


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