Why does my master node get heap memory full for inbuilt SVD API in Apache Spark during calculation of inverse of a square matrix?

I am using a three node system:

• Master node: $$64$$ GB RAM
• 2 slave node: $$32$$ GB RAM

I am calculating the inverse of a square matrix using inbuilt Apache Spark function SVD. When I called SVD function of Apache spark then $$64$$ GB memory of master node got totally consumed and start to use swap memory which makes execution too slow and eventually lead to heap memory full.

If matrix size is $$3000 \times 3000$$ then no issue but when we consider size greater than $$3000$$ (like $$3500$$ or $$5000$$) then above-mentioned issue arises.

Note: Even my MATLAB is able to compute the inverse of $$10K \times 10K$$ matrix on 32 GB RAM but Apache Spark got Memory full if I used inbuilt SVD function on above system.

Sample Code is as below:-

import org.apache.spark.mllib.linalg.{Vectors,Vector,Matrix,SingularValueDecomposition,DenseMatrix,DenseVector}
import org.apache.spark.mllib.linalg.distributed.RowMatrix

def computeInverse(X: RowMatrix): DenseMatrix = {
val nCoef = X.numCols.toInt
val svd = X.computeSVD(nCoef, computeU = true)
if (svd.s.size < nCoef) {
sys.error(s"RowMatrix.computeInverse called on singular matrix.")
}

// Create the inv diagonal matrix from S
val invS = DenseMatrix.diag(new DenseVector(svd.s.toArray.map(x => math.pow(x,-1))))

// U cannot be a RowMatrix
val U = new DenseMatrix(svd.U.numRows().toInt,svd.U.numCols().toInt,svd.U.rows.collect.flatMap(x => x.toArray))

// If you could make V distributed, then this may be better. However its alreadly local...so maybe this is fine.
val V = svd.V
// inv(X) = V*inv(S)*transpose(U)  --- the U is already transposed.
(V.multiply(invS)).multiply(U)
}

• Please post your code (the most simplified version possible that still exposes the problem). – Pete Nov 16 '17 at 2:25
• @Pete Sample code is included in the question. – Chandan Gautam Nov 18 '17 at 8:33
• I wonder what matrix inversion algorithm Spark uses. The exact solution uses O(n^2) memory, which might be the cause of the problem. (arxiv.org/pdf/1505.07570.pdf) Because of this people sometimes use approximate inverses. – tom Nov 19 '17 at 6:49
• Did you actually allocate most of the machine's memory to the driver? the default heap size isn't nearly that big. – Sean Owen Feb 23 at 1:41