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Specialization of heap objects is critical for pointer analysis to effectively analyze complex memory activity. This paper discusses heap specialization with respect to call chains. Due to the sheer number of distinct call chains, exhaustive specialization can be cumbersome. On the other hand, insufficient specialization can miss valuable opportunities to prevent spurious data flow, which results in not only reduced accuracy but also increased overhead.In determining whether further specialization will be fruitful, an object's escape information can be exploited. From empirical study, we found that restriction based on escape information is often, but not always, sufficient at prohibiting the explosive nature of specialization.For in-depth case study, four representative benchmarks are selected. For each benchmark, we vary the degree of heap specialization and examine its impact on analysis results and time. To provide better visibility into the impact, we present the points-to set and pointed-to-by set sizes in the form of histograms.

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This paper presents a new mechanism for collecting and deploying runtime optimized code. The code-collecting component resides in the instruction retirement stage and lays out hot execution paths to improve instruction fetch rate as well as enable further code optimization. The code deployment component uses an extension to the Branch Target Buffer to migrate execution into the new code without modifying the original code. No significant delay is added to the total execution of the program due to these components. The code collection scheme enables safe runtime optimization along paths that span function boundaries. This technique provides a better platform for runtime optimization than trace caches, because the traces are longer and persist in main memory across context switches. Additionally, these traces are not as susceptible to transient behavior because they are restricted to frequently executed code. Empirical results show that on average this mechanism can achieve better instruction fetch rates using only 12KB of hardware than a trace cache requiring 15KB of hardware, while producing long, persistent traces more suited to optimization.