## 4 Evaluation
### Overall Performance Analysis  
We conducted performance evaluations using state-of-the-art prefetchers for a more accurate performance evaluation. The prefetchers used for comparison are GHB, BEST-OFFSETET (BO [2], derived from Sandbox prefetcher [3]), Markov, and VLDP. For an accurate evaluation, all system configurations were kept identical, and the performance was evaluated in terms of the execution time and energy consumption.  
Figure 19 shows the results of the execution time of the proposed model and the other prefetchers. In all cases except for workload bc, our proposed model shows the best performance. The GHB and BO prefetchers show fewer prefetching attempts compared to the other three prefetcher models. Because this prefetching is not conducted at the upper cache level, which is targeted by the two aforenoted prefetchers, there may not have been many conditions suitable to trigger the prefetching. This means that the information requested by the LLC has an irregular pattern. Our proposed model shows an average performance improvement of approximately 15% compared to the GHB and BO prefetchers. Markov, VLDP, and our proposed model are memory address correlation-based prefetching methodologies. All three models show an improved execution time compared to the GHB and BO prefetchers for all workloads. This shows that correlation-based prefetchers are more efficient in dealing with LLC cache misses with much more irregular patterns than those of higher-level cache misses. However, correlation prefetching based on the history table consequently performs prefetching on regular or frequently repeated patterns over time. To overcome these weaknesses, our model adds module B, which can perform prefetching more aggressively. As a result, our proposed model shows the best performance in all cases except for bc, which has a pattern of intensive access to a specific delta.  
Figure 18: Buffer-hit count of basic prefetcher.  
On average, the performance of our model was greater by 6% and 2%, respectively, than those of the Markov and VLDP prefetchers. Figure 20 shows the results in terms of the energy consumption. In terms of energy consumption, our proposed model shows a significant improvement over the GHB and BO prefetchers. These two models and our proposed model show a good performance regarding the prefetch accuracy. However, the performance gap corresponds to a difference in coverage ratio according to the number of prefetch candidates. In other words, the number of hits in the buffer differs owing to the coverage, which causes a significant difference in terms of energy consumption. As a result, our proposed model showed an 18% performance improvement over the two aforementioned models. In addition, when compared to the Markov and VLDP prefetchers, our proposed model shows an extremely improved performance in the cases of _gcolor_ and _kcore_. These two workloads have the most irregular access pattern among those evaluated. This result shows that proposed model reacts better to irregular patterns than the other models. In addition, these two models have many opportunities to perform prefetching when accesses to other correlated lines occur. Therefore, this means that the prefetch coverage has been increased significantly owing to module B. As a result, our proposed model achieved an average improvement of 6% and 2.3%, respectively, in terms of energy consumption compared to the Markov and VLDP prefetchers.  
Figure 19: Execution time for graph processing workloads.  
Figure 20: Energy consumption for graph processing workloads.