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new f36d0c5 [SPARK-26228][MLLIB] OOM issue encountered when computing
Gramian matrix
f36d0c5 is described below
commit f36d0c56c256164f229b900778f593a0d8e4c7fc
Author: Sean Owen <sean.o...@databricks.com>
AuthorDate: Tue Jan 22 19:22:06 2019 -0600
[SPARK-26228][MLLIB] OOM issue encountered when computing Gramian matrix
Avoid memory problems in closure cleaning when handling large Gramians (>=
16K rows/cols) by using null as zeroValue
Existing tests.
Note that it's hard to test the case that triggers this issue as it would
require a large amount of memory and run a while. I confirmed locally that a
16K x 16K Gramian failed with tons of driver memory before, and didn't fail
upfront after this change.
Closes #23600 from srowen/SPARK-26228.
Authored-by: Sean Owen <sean.o...@databricks.com>
Signed-off-by: Sean Owen <sean.o...@databricks.com>
(cherry picked from commit 6dcad38ba3393188084f378b7ff6dfc12b685b13)
Signed-off-by: Sean Owen <sean.o...@databricks.com>
---
.../spark/mllib/linalg/distributed/RowMatrix.scala | 20 +++++++++++++++++---
1 file changed, 17 insertions(+), 3 deletions(-)
diff --git
a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 78a8810..5109efb 100644
---
a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++
b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -119,11 +119,25 @@ class RowMatrix @Since("1.0.0") (
val nt = if (n % 2 == 0) ((n / 2) * (n + 1)) else (n * ((n + 1) / 2))
// Compute the upper triangular part of the gram matrix.
- val GU = rows.treeAggregate(new BDV[Double](nt))(
- seqOp = (U, v) => {
+ val GU = rows.treeAggregate(null.asInstanceOf[BDV[Double]])(
+ seqOp = (maybeU, v) => {
+ val U =
+ if (maybeU == null) {
+ new BDV[Double](nt)
+ } else {
+ maybeU
+ }
BLAS.spr(1.0, v, U.data)
U
- }, combOp = (U1, U2) => U1 += U2)
+ }, combOp = (U1, U2) =>
+ if (U1 == null) {
+ U2
+ } else if (U2 == null) {
+ U1
+ } else {
+ U1 += U2
+ }
+ )
RowMatrix.triuToFull(n, GU.data)
}
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