zhengruifeng commented on pull request #30468:
URL: https://github.com/apache/spark/pull/30468#issuecomment-732093851
GEMV:
```
val ratings = srcFactorsBlocked.crossJoin(dstFactorsBlocked)
.as[(Array[Int], Array[Float], Array[Int], Array[Float])]
.mapPartitions { iter =>
var buffer: Array[Float] = null
val pq = new BoundedPriorityQueue[(Int,
Float)](num)(Ordering.by(_._2))
iter.flatMap { case (srcIds, srcMat, dstIds, dstMat) =>
require(srcMat.length == srcIds.length * rank)
require(dstMat.length == dstIds.length * rank)
val m = srcIds.length
val n = dstIds.length
if (buffer == null || buffer.length < n) {
buffer = Array.ofDim[Float](n)
}
Iterator.range(0, m).flatMap { i =>
BLAS.f2jBLAS.sgemv("T", rank, n, 1.0F, dstMat, 0, rank,
srcMat, i * rank, 1, 0.0F, buffer, 0, 1)
pq.clear()
var j = 0
while (j < n) { pq += dstIds(j) -> buffer(j); j += 1 }
val srcId = srcIds(i)
pq.iterator.map { case (dstId, value) => (srcId, dstId, value) }
}
} ++ {
buffer = null
pq.clear()
Iterator.empty
}
}
```
Then I switch to GEMV, which brings siginificent speedup. The size of
`buffer` is reduce to **n**.
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