Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17742#discussion_r114712113
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -274,46 +275,62 @@ object MatrixFactorizationModel extends
Loader[MatrixFactorizationModel] {
srcFeatures: RDD[(Int, Array[Double])],
dstFeatures: RDD[(Int, Array[Double])],
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
+ val srcBlocks = blockify(srcFeatures)
+ val dstBlocks = blockify(dstFeatures)
+ /**
+ * The previous approach used for computing top-k recommendations
aimed to group
+ * individual factor vectors into blocks, so that Level 3 BLAS
operations (gemm) could
+ * be used for efficiency. However, this causes excessive GC pressure
due to the large
+ * arrays required for intermediate result storage, as well as a high
sensitivity to the
+ * block size used.
+ * The following approach still groups factors into blocks, but
instead computes the
+ * top-k elements per block, using Level 1 BLAS (dot) and an efficient
--- End diff --
In
https://github.com/apache/spark/pull/17845/files#diff-be65dd1d6adc53138156641b610fcadaR366
I said `... using a simple dot product (instead of gemm) and an efficient
[[BoundedPriorityQueue]].`
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