Github user srowen commented on a diff in the pull request: https://github.com/apache/spark/pull/17742#discussion_r113860816 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala --- @@ -276,44 +277,53 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { num: Int): RDD[(Int, Array[(Int, Double)])] = { 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 + /** + * Use dot to replace blas 3 gemm is the key approach to improve efficiency. + * By this change, we can get the topK elements of each block to reduce the GC time. + * Comparing with BLAS.dot, hand-written dot is high efficiency. + */ + val ratings = srcBlocks.cartesian(dstBlocks).flatMap { case (srcIter, dstIter) => + val m = srcIter.size + val n = math.min(dstIter.size, num) + val output = new Array[(Int, (Int, Double))](m * n) + var j = 0 + srcIter.foreach { case (srcId, srcFactor) => + val pq = new BoundedPriorityQueue[(Int, Double)](n)(Ordering.by(_._2)) --- End diff -- Nit: there are several 4-space indents here that should be 2
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