# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Dataset
#### 5.1.2 Synthetic Datasets  
Synthetic datasets are generated by specific algorithms, which are used to simulate datasets generated with specific types of workloads. These datasets allow the investigator to control the various parameters of the load to test the performance of the system in extreme or special cases. Synthetic datasets are of great value for studying system limit performance, verifying theoretical models, or developing new scheduling methods. In the experiment of the task-scheduling method of the heterogeneous chip, the synthetic dataset can be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Max Width** & **Depth** & **Numbers of Nodes** & **Supported Accelerators** \\ \hline WiFi TX & 5 & 7 & 27 & IFFT \\ \hline WiFi RX & 5 & 10 & 34 & FF, Viterbi Decoder \\ \hline Radar Correlator & 2 & 6 & 7 & FFT, IFFT \\ \hline Temporal Mitigation & 2 & 6 & 10 & Matrix Multiply \\ \hline Single Carrier TX & 1 & 8 & 8 & \\ \hline Single Carrier RX & 1 & 8 & 8 & Viterbi Decoder \\ \hline \hline \end{tabular}
\end{table}
Table 4: An example of the real load dataset [37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Type** & **Data Characteristics** & **Scenario Complexity** & **Limitation** \\ \hline Real Load & \begin{tabular}{c} Dynamic heterogeneity \\ with real-world \\ task features \\ \end{tabular} & \begin{tabular}{c} Highest complexity \\ (adaptation to dynamic \\ environments) \\ \end{tabular} & \begin{tabular}{c} High acquisition cost and \\ privacy sensitivity \\ \end{tabular} \\ \hline Synthetic Dataset & \begin{tabular}{c} Adjustable parameters and \\ controllable distributions \\ \end{tabular} & \begin{tabular}{c} Moderate complexity \\ (customizable scenarios) \\ \end{tabular} & \begin{tabular}{c} Potential deviation from \\ real-world conditions \\ \end{tabular} \\ \hline Standard Benchmark & \begin{tabular}{c} Standardized structures \\ for reproducibility \\ \end{tabular} & \
\begin{tabular}{c} Low complexity \\ (controlled and \\ fixed scenarios) \\ \end{tabular} &
\begin{tabular}{c} Over-idealization and \\ limited diversity coverage \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Common datasets for task-scheduling methods.  
customized according to specific research purposes and requirements, simulate various task scenarios and data distribution, and carry out targeted tests on specific task-scheduling algorithms or extreme situations.  
For example, [PERSON] et al. [38] provided a method based on the layer-by-layer method, improving it to obtain a random directed acyclic graph (DAG) task set. [PERSON] et al. [39] used the open-source DAG generation software Daggen ([[https://github.com/frs69](https://github.com/frs69) wq/daggen]([https://github.com/frs69](https://github.com/frs69) wq/daggen) ) [40] to generate a random DAG. Synthetic datasets are more customizable to simulate various situations to help evaluate the performance of task-scheduling algorithms under extreme conditions. However, the synthetic dataset is built based on assumptions and simulation, and there is a certain gap between its data generation mode and the real-world application scenarios, so it is difficult to comprehensively cover the complex task types, data characteristics, and user behavior patterns in practical applications. Therefore, there is insufficient confidence in the experimental results of the synthetic dataset.