Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Distributed-Memory Parallel Matrix-Vector Multiplication with
  Wide or Tall Unstructured Sparse Matrices   This paper presents an efficient technique for matrix-vector and
vector-transpose-matrix multiplication in distributed-memory parallel computing
environments, where the matrices are unstructured, sparse, and have a
substantially larger number of columns than rows or vice versa. Our method
allows for parallel I/O, does not require extensive preprocessing, and has the
same communication complexity as matrix-vector multiplies with column or row
partitioning. Our implementation of the method uses MPI. We partition the
matrix by individual nonzero elements, rather than by row or column, and use an
"overlapped" vector representation that is matched to the matrix. The transpose
multiplies use matrix-specific MPI communicators and reductions that we show
can be set up in an efficient manner. The proposed technique achieves a good
work per processor balance even if some of the columns are dense, while keeping
communication costs relatively low.

34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
2
The categories of this paper are: math
7
The categories of this paper are: hep-th