  As the interest to Graph Neural Networks (GNNs) is growing, the importance of
benchmarking and performance characterization studies of GNNs is increasing. So
far, we have seen many studies that investigate and present the performance and
computational efficiency of GNNs. However, the work done so far has been
carried out using a few high-level GNN frameworks. Although these frameworks
provide ease of use, they contain too many dependencies to other existing
libraries. The layers of implementation details and the dependencies complicate
the performance analysis of GNN models that are built on top of these
frameworks, especially while using architectural simulators. Furthermore,
different approaches on GNN computation are generally overlooked in prior
characterization studies, and merely one of the common computational models is
evaluated. Based on these shortcomings and needs that we observed, we developed
a benchmark suite that is framework independent, supporting versatile
computational models, easily configurable and can be used with architectural
simulators without additional effort.
  Our benchmark suite, which we call gSuite, makes use of only hardware
vendor's libraries and therefore it is independent of any other frameworks.
gSuite enables performing detailed performance characterization studies on GNN
Inference using both contemporary GPU profilers and architectural GPU
simulators. To illustrate the benefits of our new benchmark suite, we perform a
detailed characterization study with a set of well-known GNN models with
various datasets; running gSuite both on a real GPU card and a timing-detailed
GPU simulator. We also implicate the effect of computational models on
performance. We use several evaluation metrics to rigorously measure the
performance of GNN computation.

Machine Learning;Performance