GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN System

Yidong Gong, Pradeep Kumar
4/5/2024
cs.LGcs.DC

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

We hypothesize that the absence of a standardized benchmark has allowed several fundamental pitfalls in GNN System design and evaluation that the community has overlooked. In this work, we propose GNNBench, a plug-and-play benchmarking platform focused on system innovation. GNNBench presents a new protocol to exchange their captive tensor data, supports custom classes in System APIs, and allows automatic integration of the same system module to many deep learning frameworks, such as PyTorch and TensorFlow. To demonstrate the importance of such a benchmark framework, we integrated several GNN systems. Our results show that integration with GNNBench helped us identify several measurement issues that deserve attention from the community.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Single-GPU GNN Systems: Traps and Pitfalls

85% match2024

GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization

80% match1 repo252023

NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments

80% match2023

ConsumerBench: Benchmarking Generative AI Applications on End-User Devices

80% match1 repo222025

BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs

79% match2023

Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness

78% match1 repo3052023

Cite this paper

@article{gong2024gnnbench,
  title  = {GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN System},
  author = {Yidong Gong and Pradeep Kumar},
  year   = {2024},
  eprint = {2404.04118},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.04118v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.04118.svg)](https://codeofpaper.com/paper/2404.04118)

Discussion