Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Sofware-Hardware Techniques

Zhangxiaowen Gong, Houxiang Ji, Yao Yao, Christopher W. Fletcher, Christopher J. Hughes, Josep Torrellas

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graph Neural Networks (GNNs) are becoming popular because they are effective at extracting information from graphs. To execute GNNs, CPUs are good platforms because of their high availability and terabyte-level memory capacity, which enables full-batch computation on large graphs. However, GNNs on CPUs are heavily memory bound, which limits their performance. In this paper, we address this problem by alleviating the stress of GNNs on memory with cooperative software-hardware techniques. Our software techniques include: (i) layer fusion that overlaps the memory-intensive phase and the compute-intensive phase in a GNN layer, (ii) feature compression that reduces memory trafc by exploiting the sparsity in the vertex feature vectors, and (iii) an algorithm that changes the processing order of vertices to improve temporal locality. On top of the software techniques, we enhance the CPUs' direct memory access (DMA) engines with the capability to execute the GNNs' memory-intensive phase, so that the processor cores can focus on the compute-intensive phase. We call the combination of our software and hardware techniques Graphite. We evaluate Graphite with popular GNN models on large graphs. The result is high-performance full-batch GNN training and inference on CPUs. Our software techniques outperform a state-of-theart GNN layer implementation by 1.7-1.9x in inference and 1.6-2.6x in training. Our combined software and hardware techniques speedup inference by 1.6-2.0x and training by 1.9-3.1x.

Original languageEnglish (US)
Title of host publicationISCA 2022 - Proceedings of the 49th Annual International Symposium on Computer Architecture
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages16
ISBN (Electronic)9781450386104
StatePublished - Jun 18 2022
Event49th IEEE/ACM International Symposium on Computer Architecture, ISCA 2022 - New York, United States
Duration: Jun 18 2022Jun 22 2022

Publication series

NameProceedings - International Symposium on Computer Architecture
ISSN (Print)1063-6897


Conference49th IEEE/ACM International Symposium on Computer Architecture, ISCA 2022
Country/TerritoryUnited States
CityNew York


  • CPU
  • DMA
  • Graph Neural Networks
  • Hardware-software co-design

ASJC Scopus subject areas

  • Hardware and Architecture


Dive into the research topics of 'Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Sofware-Hardware Techniques'. Together they form a unique fingerprint.

Cite this