Optimizing Out-Of-Memory Sparse-Dense Matrix Multiplication
We will examine state-of-the-art approaches for sparse-dense matrix multiplication (SpMDM), with a focused application on graph machine learning workloads, such as graph neural networks (GNNs), though this work is general enough such that it should apply to any application tailored for running matri...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Massachusetts Institute of Technology
2023
|
| Online Access: | https://hdl.handle.net/1721.1/151318 |