  CPU performance prediction, which involves forecasting the performance scores
of a CPU based on its hardware characteristics during its operation, is a
critical technology for computational system design and resource management in
the big data era. 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. In the research process, this field lacks a
standard dataset with unified hardware characteristics, wide data coverage, and
comprehensive benchmarks. Second, existing methods based on hardware simulation
models or machine learning exhibit notable shortcomings, such as lengthy
simulation test cycles and low prediction accuracy. 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 deep learning
based model called Nova CPU Performance Predictor (NCPP) as the baseline for
this new dataset. The NCPP network is designed based on group attention
mechanism. It effectively quantifies the implicit relationships between
hardware characteristics within and across groups and comprehensively models
the impact of various hardware characteristics on CPU performance prediction.
We conduct comparative experiments using the proposed PerfCastDB dataset.
Compared to existing approaches, NCPP achieves superior evaluation results,
demonstrating its effectiveness. Furthermore, we have open-sourced part of the
dataset and the NCPP network code to facilitate subsequent research. The
resources can be accessed at https://github.com/xiaoman-liu/NCPP.
