CPU performance prediction, which involves forecasting the performance scores of a CPU based on its hardware characteristics during the operation process, is a critical technology for computational system design and resource management. However, this research field currently faces two significant challenges. First, collecting real-world data is challenging due to the wide variety of CPU products on the market and the highly specialized nature of relevant hardware characteristics. Second, existing methods based on hardware simulation models or machine learning exhibit notable shortcomings, such as lengthy simulation test cycles, low prediction accuracy, and the ignoration of characteristic correlations. To bridge these gaps, we first collect, preprocess, and standardize historical data from the 4th Generation Intel Xeon Scalable Processors across multiple benchmark suites to create a new dataset, named PerfCastDB. Subsequently, we design a novel network MambaCPU (MaC) as the baseline for the PerfCastDB dataset. This model leverages the mamba structure to mine the global dependencies and correlations between multiple characteristics. The intra- and inter-group attention mechanisms are subsequently utilized to refine the correlations within and across the characteristic type groups. These techniques enhance the analysis and mining capability of Mac for the complex multivariate correlations. Comparative experiments on the PerfCastDB dataset demonstrate that MaC achieves superior results compared to existing methods, validating its effectiveness. Furthermore, we have open-sourced part of the dataset and the MaC code at \url{https://github.com/xiaoman-liu/MaC} to facilitate the subsequent research.

翻译：CPU性能预测是一项关键技术，涉及根据CPU在运行过程中的硬件特性预测其性能得分，对计算系统设计和资源管理至关重要。然而，该研究领域目前面临两大挑战。首先，由于市场上CPU产品种类繁多且相关硬件特性高度专业化，收集真实数据十分困难。其次，现有基于硬件仿真模型或机器学习的方法存在明显不足，例如仿真测试周期长、预测精度低以及忽略特性间相关性。为弥补这些不足，我们首先从第四代英特尔至强可扩展处理器在多个基准测试套件中的历史数据中收集、预处理并标准化数据，构建了一个名为PerfCastDB的新数据集。随后，我们设计了一种新颖的网络MambaCPU（MaC）作为PerfCastDB数据集的基线模型。该模型利用mamba结构挖掘多个特性间的全局依赖性和相关性。随后采用组内和组间注意力机制来细化特性类型组内和跨组的相关性。这些技术增强了MaC对复杂多元相关性的分析和挖掘能力。在PerfCastDB数据集上的对比实验表明，MaC相比现有方法取得了更优的结果，验证了其有效性。此外，我们已在\url{https://github.com/xiaoman-liu/MaC}开源了部分数据集和MaC代码，以促进后续研究。