Dynamo: a transparent dynamic optimization system
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Adaptive optimization in the Jalapeño JVM
OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
An infrastructure for adaptive dynamic optimization
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
The Performance of Runtime Data Cache Prefetching in a Dynamic Optimization System
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Pin: building customized program analysis tools with dynamic instrumentation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Process-shared and persistent code caches
Proceedings of the fourth ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
MAJOR: rapid tool development with aspect-oriented programming
PPPJ '09 Proceedings of the 7th International Conference on Principles and Practice of Programming in Java
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Dynamic code transformation systems (DCTS) can broadly be grouped into three distinct categories: optimization, translation and instrumentation. All of these face the critical challenge of minimizing the overhead incurred during transformation since their execution is interleaved with the execution of the application itself. The common DCTS tasks incurring overhead are the identification of frequently executed code sequences, costly analysis of program information, and run-time creation (writing) of new code sequences. The cost of such work is amortized by the repeated execution of the transformed code. However, as these steps are applied to all general code regions (regardless of their execution frequency and characteristics), there is substantial overhead that impacts the application's performance. As such, it is challenging to effectively deploy dynamic transformation under fixed performance constraints. This paper explores a technique for eliminating the overhead incurred by exploiting persistent application execution characteristics that are shared across different application invocations. This technique is implemented and evaluated in Pin, a dynamic instrumentation engine. This version of Pin is referred to as Persistent Pin (PPin). Initial PPin experimental results indicate that using information from prior runs can reduce dynamic instrumentation overhead of SPEC applications by as much as 25% and over 90% for everyday applications like web browsers, display rendering systems, and spreadsheet programs.