| |
| META TOPICPARENT |
name="WebHome" |
Computer Architecture
Software Optimization |
|
<
< | Previous research work has identified memory bandwidth as the main bottleneck of the ubiquitous Sparse Matrix-Vector Multiplication kernel. To attack this problem, we aim at reducing the overall data volume of the algorithm. Typical sparse matrix representation schemes store only the non-zero elements of the matrix and employ additional indexing information to properly iterate over these elements. In this paper we propose two distinct compression methods targeting index and numerical values respectively. We perform a set of experiments on a large real-world matrix set and demonstrate that the index compression method can be applied successfully to a wide range of matrices. Moreover, the value compression method is able to achieve impressive speedups in a more limited yet important class of sparse matrix that contain a small number of distinct values. |
>
> | Previous research work has identified memory bandwidth as the main bottleneck of the ubiquitous Sparse Matrix-Vector Multiplication kernel. To attack this problem, we aim at reducing the overall data volume of the algorithm. Typical sparse matrix representation schemes store only the non-zero elements of the matrix and employ additional indexing information to properly iterate over these elements. In this paper we propose two distinct compression methods targeting index and numerical values respectively. We perform a set of experiments on a large real-world matrix set and demonstrate that the index compression method can be applied successfully to a wide range of matrices. Moreover, the value compression method is able to achieve impressive speedups in a more limited, yet important, class of sparse matrices that contain a small number of distinct values. |
| |
Operating Systems |
