# RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

arXiv: https://arxiv.org/abs/2311.18141 · HTML: https://mlsanity.com/paper/2311.18141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication](https://mlsanity.com/paper/2510.08874/) (2025)
- [Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs](https://mlsanity.com/paper/2601.17136/) (2026)

## Abstract (arXiv)

Sparse matrix multiplication is an important kernel for large-scale graph processing and other data-intensive applications. In this paper, we implement various asynchronous, RDMA-based sparse times dense (SpMM) and sparse times sparse (SpGEMM) algorithms, evaluating their performance running in a distributed memory setting on GPUs. Our RDMA-based implementations use the NVSHMEM communication library for direct, asynchronous one-sided communication between GPUs. We compare our asynchronous implementations to state-of-the-art bulk synchronous GPU libraries as well as a CUDA-aware MPI implementation of the SUMMA algorithm. We find that asynchronous RDMA-based implementations are able to offer favorable performance compared to bulk synchronous implementations, while also allowing for the straightforward implementation of novel work stealing algorithms.