The performance analytics domain in High Performance Computing (HPC) uses tabular data to solve regression problems, such as predicting the execution time. Existing Machine Learning (ML) techniques leverage the correlations among features given tabular datasets, not leveraging the relationships between samples directly. Moreover, since high-quality embeddings from raw features improve the fidelity of the downstream predictive models, existing methods rely on extensive feature engineering and pre-processing steps, costing time and manual effort. To fill these two gaps, we propose a novel idea of transforming tabular performance data into graphs to leverage the advancement of Graph Neural Network-based (GNN) techniques in capturing complex relationships between features and samples. In contrast to other ML application domains, such as social networks, the graph is not given; instead, we need to build it. To address this gap, we propose graph-building methods where nodes represent samples, and the edges are automatically inferred iteratively based on the similarity between the features in the samples. We evaluate the effectiveness of the generated embeddings from GNNs based on how well they make even a simple feed-forward neural network perform for regression tasks compared to other state-of-the-art representation learning techniques. Our evaluation demonstrates that even with up to 25% random missing values for each dataset, our method outperforms commonly used graph and Deep Neural Network (DNN)-based approaches and achieves up to 61.67% & 78.56% improvement in MSE loss over the DNN baseline respectively for HPC dataset and Machine Learning Datasets.

翻译：高性能计算（HPC）领域的性能分析通常使用表格数据解决回归问题（如预测执行时间）。现有机器学习（ML）技术仅利用表格数据集中特征间的相关性，而未直接利用样本之间的关系。此外，由于原始特征的高质量嵌入能提升下游预测模型的保真度，现有方法依赖大量特征工程和预处理步骤，耗费时间与人力。为填补这两项空白，我们提出一种创新思路：将表格性能数据转化为图结构，从而利用基于图神经网络（GNN）的技术在捕捉特征与样本间复杂关系方面的优势。与其他ML应用领域（如社交网络）不同，此处图并非预设，而需自行构建。为此，我们提出图构建方法：节点代表样本，边基于样本特征间的相似度通过迭代自动推断。我们通过评估对比其他先进表示学习技术，检验GNN生成嵌入的有效性——即使采用简单的前馈神经网络进行回归任务，其性能表现依然出色。实验表明，即使在每个数据集存在高达25%随机缺失值的情况下，我们的方法仍优于常见的图与深度神经网络（DNN）方法；在HPC数据集与机器学习数据集上，相较于DNN基线，MSE损失分别降低61.67%与78.56%。