The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopting the latest machine learning methods is the representation of programming languages, which directly impacts the ability of machine learning methods to reason about programs. The absence of numerical awareness, composite data structure information, and improper way of presenting variables in previous representation works have limited their performances. To overcome the limitations and challenges of current program representations, we propose a novel graph-based program representation called PERFOGRAPH. PERFOGRAPH can capture numerical information and the composite data structure by introducing new nodes and edges. Furthermore, we propose an adapted embedding method to incorporate numerical awareness. These enhancements make PERFOGRAPH a highly flexible and scalable representation that can effectively capture program intricate dependencies and semantics. Consequently, it serves as a powerful tool for various applications such as program analysis, performance optimization, and parallelism discovery. Our experimental results demonstrate that PERFOGRAPH outperforms existing representations and sets new state-of-the-art results by reducing the error rate by 7.4% (AMD dataset) and 10% (NVIDIA dataset) in the well-known Device Mapping challenge. It also sets new state-of-the-art results in various performance optimization tasks like Parallelism Discovery and Numa and Prefetchers Configuration prediction.

本研究提出了一种名为PERFOGRAPH的基于图形的程序表示方法，通过引入新的节点和边来捕获数值信息和组合数据结构，进一步提出了一种改进的嵌入方法来合并数值意识，使其成为一个高度灵活和可扩展的表示方法，以有效地捕获程序的细节依赖关系和语义信息，从而可以作为程序分析、性能优化和并行性发现等各种应用领域的强有力工具，并通过多种实验验证，证明了它在错误率和性能优化等方面的优越性。

PERFOGRAPH：适用于性能优化和程序分析的数值感知程序图表示