# FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks

arXiv: https://arxiv.org/abs/2011.06391 · HTML: https://mlsanity.com/paper/2011.06391/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 26 of 26)

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- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations](https://mlsanity.com/paper/2210.10737/) (2022)
- [PhysGraph: Physics-Based Integration Using Graph Neural Networks](https://mlsanity.com/paper/2301.11841/) (2023)
- [BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs](https://mlsanity.com/paper/2305.02522/) (2023)
- [Distributed-Memory Sparse Kernels for Machine Learning](https://mlsanity.com/paper/2203.07673/) (2022)
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- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [Scalable Graph Convolutional Network Training on Distributed-Memory Systems](https://mlsanity.com/paper/2212.05009/) (2022)
- [DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs](https://mlsanity.com/paper/2411.16127/) (2024)
- [Fused3S: Fast Sparse Attention on Tensor Cores](https://mlsanity.com/paper/2505.08098/) (2025)
- [SENSEi: Input-Sensitive Compilation for Accelerating GNNs](https://mlsanity.com/paper/2306.15155/) (2023)
- [Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU](https://mlsanity.com/paper/2311.00368/) (2023)
- [iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations](https://mlsanity.com/paper/2403.14853/) (2024)
- [Architectural Implications of Embedding Dimension during GCN on CPU and GPU](https://mlsanity.com/paper/2212.00827/) (2022)
- [GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU](https://mlsanity.com/paper/2404.03019/) (2024)
- [MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators](https://mlsanity.com/paper/2501.01951/) (2025)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)
- [Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures](https://mlsanity.com/paper/2504.09870/) (2025)
- [FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow](https://mlsanity.com/paper/2511.04768/) (2025)
- [AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention](https://mlsanity.com/paper/2511.17594/) (2025)
- [FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics](https://mlsanity.com/paper/2602.13140/) (2026)
- [Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs](https://mlsanity.com/paper/2604.16715/) (2026)

## Abstract (arXiv)

We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedMM. By using user-defined functions, FusedMM can capture almost all computational patterns needed by popular graph embedding and GNN approaches. FusedMM is an order of magnitude faster than its equivalent kernels in Deep Graph Library. The superior performance of FusedMM comes from the low-level vectorized kernels, a suitable load balancing scheme and an efficient utilization of the memory bandwidth. FusedMM can tune its performance using a code generator and perform equally well on Intel, AMD and ARM processors. FusedMM speeds up an end-to-end graph embedding algorithm by up to 28x on different processors.