Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/17742#discussion_r113428386 --- 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) { --- End diff -- Space here: `while (`
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