Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
CoRR(2024)
摘要
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.
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