As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of source code, these representations do not understand how code dynamically executes. In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution. Our approach defines a multi-task GNN over low-level representations of source code and program state (i.e., assembly code and dynamic memory states), converting complex source code constructs and complex data structures into a simpler, more uniform format. We show that this leads to improved performance over similar methods that do not use execution and it opens the door to applying GNN models to new tasks that would not be feasible from static code alone. As an illustration of this, we apply the new model to challenging dynamic tasks (branch prediction and prefetching) from the SPEC CPU benchmark suite, outperforming the state-of-the-art by 26% and 45% respectively. Moreover, we use the learned fused graph embeddings to demonstrate transfer learning with high performance on an indirectly related task (algorithm classification).

本篇论文提出利用图神经网络（Graph Neural Networks，GNNs）来学习通用源代码及其执行的融合表示的新方法，通过一个定义于低级表示的源代码和程序状态的多任务GNN，成功地将复杂的源代码结构和复杂的数据结构转换为更简单，更一致的格式，引领GNN的新应用领域，其应用到具有挑战性的动态任务（SPEC CPU基准套件中的分支预测和预抓取），超越现有技术水平26％和45％，并用于表现高性能的传输学习间接相关任务（算法分类）。

通过神经代码融合学习执行