Comment out non distributed version of mult

Comment out the non ditributed version of the sparse matrix
multiplication and also reduce the problem size in the test to make sure
we don't run out of memory

Signed-off-by: Shreyas Khandekar <60454060+ShreyasKhandekar@users.noreply.github.com>

src/SparseMatrix.chpl

@@ -57,12 +57,12 @@ module SparseMatrix {
   // of nonzeroes to be passed in and updated rather than assuming that
   // the multiplication is the first/only step.
   //
-  proc sparseMatMatMult(A, B, ref spsData) {
-    forall ac_br in A.cols() with (merge reduce spsData) do
-      for (ar, a) in A.rowsAndVals(ac_br) do
-        for (bc, b) in B.colsAndVals(ac_br) do
-          spsData.add((ar, bc), a * b);
-  }
+  // proc sparseMatMatMult(A, B, ref spsData) {
+  //   forall ac_br in A.cols() with (merge reduce spsData) do
+  //     for (ar, a) in A.rowsAndVals(ac_br) do
+  //       for (bc, b) in B.colsAndVals(ac_br) do
+  //         spsData.add((ar, bc), a * b);
+  // }
 
   proc sparseMatMatMult(A, B) where (!A.chpl_isNonDistributedArray() &&
                                      !B.chpl_isNonDistributedArray()) {

tests/sparse_test.py

@@ -73,8 +73,8 @@ def matmatmult(rowsA, colsA, valsA, rowsB, colsB, valsB):
             result_rows, result_cols, result_vals = zip(*[(r, c, v) for (r, c), v in result.items()])
 
             return list(result_rows), list(result_cols), list(result_vals)
-        matA = ak.random_sparse_matrix(100, 1, 'CSC') # Make it fully dense to make testing easy
-        matB = ak.random_sparse_matrix(100, 1, 'CSR') # Make it fully dense to make testing easy
+        matA = ak.random_sparse_matrix(10, 1, 'CSC') # Make it fully dense to make testing easy
+        matB = ak.random_sparse_matrix(10, 1, 'CSR') # Make it fully dense to make testing easy
         fill_vals_a = ak.randint(0, 10, matA.nnz)
         fill_vals_b = ak.randint(0, 10, matB.nnz)
         matA.fill_vals(fill_vals_a)