[GitHub] [spark] zhengruifeng edited a comment on pull request #30468: [SPARK-33518][ML][WIP] Improve performance of ML ALS recommendForAll by GEMV

[GitBox <gi...@apache.org>]

zhengruifeng edited a comment on pull request #30468:
URL: https://github.com/apache/spark/pull/30468#issuecomment-732093851


   [GEMV](https://github.com/apache/spark/pull/30468/commits/b6459683e734af3749be3b9ce6047ca22fabfdd9):
   ```
   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**.
   
   ![als_gemv_jobs_2020_11_23_16_55_10](https://user-images.githubusercontent.com/7322292/99955591-9236cb00-2dbf-11eb-84f2-3a2421412a48.png)
   
   ![als_gemv_exe_2020_11_23_16_57_13](https://user-images.githubusercontent.com/7322292/99955609-99f66f80-2dbf-11eb-8ba6-fa64d6e77fa8.png)
   


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