Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
<|reference_start|>Accelerating Graph Neural Networks on Real Processing-In-Memory Systems: Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. In this work, we introduce PyGim, an efficient ML framework that accelerates GNNs on real PIM systems. We propose intelligent parallelization techniques for memory-intensive kernels of GNNs tailored for real PIM systems, and develop handy Python API for them. We provide hybrid GNN execution, in which the compute-intensive and memory-intensive kernels are executed in processor-centric and memory-centric computing systems, respectively, to match their algorithmic nature. We extensively evaluate PyGim on a real-world PIM system with 1992 PIM cores using emerging GNN models, and demonstrate that it outperforms its state-of-the-art CPU counterpart on Intel Xeon by on average 3.04x, and achieves higher resource utilization than CPU and GPU systems. Our work provides useful recommendations for software, system and hardware designers. PyGim will be open-sourced to enable the widespread use of PIM systems in GNNs.<|reference_end|>
@article{giannoula2024pygim:,
    title={PyGim: An Efficient Graph Neural Network Library for Real
  Processing-In-Memory Architectures},
    author={Christina Giannoula, Peiming Yang, Ivan Fernandez Vega, Jiacheng Yang,
  Sankeerth Durvasula, Yu Xin Li, Mohammad Sadrosadati, Juan Gomez Luna, Onur
  Mutlu, Gennady Pekhimenko},
    journal={arXiv preprint arXiv:2402.16731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16731},
    primaryClass={cs.AR cs.DC cs.LG cs.PF}
}