Abstract—Modern languages like Java and C # rely on dynamic optimizations in virtual machines for better performance. Current dynamic optimizations are reactive. Their performance is constrained by the dependence on runtime sampling and the partial knowledge of the execution. This work tackles the problems by developing a set of techniques that make a virtual machine evolve across production runs. The virtual machine incrementally learns the relation between program inputs and optimization strategies so that it proactively predicts the optimizations suitable for a new run. The prediction is discriminative, guarded by confidence measurement through dynamic self-evaluation. We employ an enriched extensible specification language to resolve the complexities in program inputs. These techniques, implemented in Jikes RVM, produce significant performance improvement on a set of Java applications.

Dynamic program optimizations are critical for the efficiency of applications in managed programming languages and scripting languages. Recent studies have shown that exploitation of program inputs may enhance the effectiveness of dynamic optimizations significantly. However, current solutions for enabling the exploitation require either programmers’ annotations or intensive offline profiling, impairing the practical adoption of the techniques. This current work examines the basic feasibility of transparent integration of input-consciousness into dynamic program optimizations, particularly in managed execution environments. It uses transparent learning across production runs as the basic vehicle, and investigates the implications of cross-run learning on each main component of inputconscious

Chip-multiprocessors (CMPs) are quickly becoming entrenched as the main-stream architectural platform in computer systems. One of the critical challenges facing CMPs is designing applications to effectively leverage the computational resources they provide. Modifying applications to effectively run on CMPs requires understanding the bottlenecks in applications, which necessitates a detailed understanding of architectural features. Unfortunately, identifying bottlenecks is complex and often requires enumerating a wide range of behaviors. To assist in identifying bottlenecks, this paper presents a framework for developing analytical models based on dynamic program behaviors. That is, given a program and set of training inputs, the framework will generate several analytical models that accurately predict online program behaviors such as memory utilization and synchronization overhead, while taking program input into consideration. These models can prove invaluable for online optimization systems and input-specific analysis of program behavior. We demonstrate that this framework is practical and accurate on a wide range of synthetic and real-world parallel applications over various workloads. 1