# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 4 Evaluations
### Overall Performance Analysis  
To validate our proposed model, we conducted a comparison between various prefetch methods, such as GHB, SMS, AMPM, Best-Offset, which are recognized as one of the state-of-the-art prefetchers, and Dynamic Recognition (DynamicR) [7; 13; 17]. In addition, we employed the LRU policy as the common cache replacement policy for all the models.  
As shown in Figure 11, our proposed model outperformed all other compared models in execution times for all workloads that we have evaluated. Specifically, for the conventional DRAM model, our proposed model demonstrated a significant improvement of 56% in the execution time.  
Our proposed model outperformed the conventional prefetching models, achieving a 16% and 15% improvement in execution time over GHB and SMS, respectively. In addition, compared to AMPM and Best-Offset, the proposed model exhibited a 5% and 6% improvement in execution time, respectively. Notably, the proposed model demonstrated superior performance in execution time for both the degree-centrality (DC) and triangle-count (TC) workloads, which involve complex memory streams and intensive memory requests. Even with the advanced DynamicR prefetch engine, the performance improvement was about 3%.  
Figure 12 presents a detailed comparison of energy consumption for various prefetcher-based hybrid main memory models, including our proposed one with various traditional prefetcher-based models. The experimental results indicate that our proposed model surpasses other comparable models through all workloads. The proposed model shows a 57% improvement in energy consumption compared to the conventional DRAM model. In comparison to existing prefetching models, the proposed model significantly surpasses their energy consumption performance, exhibiting a 16% and 15% better performance than GHB and SMS-based models, respectively. Furthermore, the proposed model out  
Figure 11: Execution time for graph processing.  
Figure 10: Execution time for different regression prefetchers.  
performs AMPM and Best-Offset prefetcher-based hybrid memory models, with 5% and 5.5% lower energy consumption, respectively. Moreover, the proposed model demonstrates superior energy efficiency compared to hybrid memory system models based on previous prefetchers, such as the AMPM and Best-Offset prefetchers, with a reduction in energy consumption of 5% and 5.5%, respectively. Overall, the proposed model showcases the best energy consumption performance among the evaluated models for the TC workload. Even when compared to the most advanced DynamicR prefetch engine, the proposed model demonstrates a 2% improvement in energy consumption, emphasizing its efficiency in terms of power consumption.  
Figure 13 compares the total hit rates of various prefetching methods, including our proposed model. The results show that our model outperformed conventional hybrid memory models based on various prefetchers, achieving an approximate 5% improvement compared to the conventional DRAM model. Our proposed model also achieved, on average, 2% improvement compared to other advanced models in terms of hit ratios. Notably, even the most advanced DynamicR prefetch engine only exhibited an improvement of approximately 0.3%, highlighting the significant effectiveness of our proposed model in improving hit rates.  
The advanced point of our prefetch model had three primary reasons:  
1. From a global perspective, pattern recognition was performed for different patterns, and three different prefetch engines were designed.
2. Local linear regression was optimized and improved, and an intelligent regression algorithm for memory prefetching in irregular patterns was proposed.  
Figure 12: Energy consumption for graph processing.  
Figure 13: The total hit rate for graph processing.  
Several experiments were performed to choose the most suitable parameter configuration.  
In conclusion, the proposed model demonstrated superior performance compared to previous models due to its intelligent linear regression prefetching schemes.