  Identifying errors in parallel MPI programs is a challenging task. Despite
the growing number of verification tools, debugging parallel programs remains a
significant challenge. This paper is the first to utilize embedding and deep
learning graph neural networks (GNNs) to tackle the issue of identifying bugs
in MPI programs. Specifically, we have designed and developed two models that
can determine, from a code's LLVM Intermediate Representation (IR), whether the
code is correct or contains a known MPI error. We tested our models using two
dedicated MPI benchmark suites for verification: MBI and MPI-CorrBench. By
training and validating our models on the same benchmark suite, we achieved a
prediction accuracy of 92% in detecting error types. Additionally, we trained
and evaluated our models on distinct benchmark suites (e.g., transitioning from
MBI to MPI-CorrBench) and achieved a promising accuracy of over 80%. Finally,
we investigated the interaction between different MPI errors and quantified our
models' generalization capabilities over new unseen errors. This involved
removing error types during training and assessing whether our models could
still predict them. The detection accuracy of removed errors varies
significantly between 20% to 80%, indicating connected error patterns.
