This paper presents a combined pointer and escape analysis algorithm for Java programs. The algorithm is based on the abstraction of points-to escape graphs, which characterize how local variables and fields in objects refer to other objects. Each points-to escape graph also contains escape information, which characterizes how objects allocated in one region of the program can escape to be accessed by another region. The algorithm is designed to analyze arbitrary regions of complete or incomplete programs, obtaining complete information for objects that do not escape the analyzed regions.We have developed an implementation that uses the escape information to eliminate synchronization for objects that are accessed by only one thread and to allocate objects on the stack instead of in the heap. Our experimental results are encouraging.We were able to analyze programs tens of thousands of lines long. For our benchmark programs, our algorithms enable the elimination of between 24% and 67% of the synchronization operations. They also enable the stack allocation of between 22% and 95% of the objects.

We present PowerDial, a system for dynamically adapting application behavior to execute successfully in the face of load and power fluctuations. PowerDial transforms static configuration parameters into dynamic knobs that the PowerDial control system can manipulate to dynamically trade off the accuracy of the computation in return for reductions in the computational resources that the application requires to produce its results. These reductions translate directly into performance improvements and power savings.Our experimental results show that PowerDial can enable our benchmark applications to execute responsively in the face of power caps that would otherwise significantly impair responsiveness. They also show that PowerDial can significantly reduce the number of machines required to service intermittent load spikes, enabling reductions in power and capital costs.

This paper presents a new combined pointer and escape analysis for multithreaded programs. The algorithm uses a new abstraction called parallel interaction graphs to analyze the interactions between threads and extract precise points-to, escape, and action ordering information for objects accessed by multiple threads. The analysis is compositional, analyzing each method or thread once to extract a parameterized analysis result that can be specialized for use in any context. It is also capable of analyzing programs that use the unstructured form of multithreading present in languages such as Java and standard threads packages such as POSIX threads.We have implemented the analysis in the MIT Flex compiler for Java and used the extracted information to 1) verify that programs correctly use region-based allocation constructs, 2) eliminate dynamic checks associated with the use of regions, and 3) eliminate unnecessary synchronization. Our experimental results show that analyzing the interactions between threads significantly increases the effectiveness of the region analysis and region check elimination, but has little effect for synchronization elimination.

This paper presents a novel framework for the symbolic bounds analysis of pointers, array indices, and accessed memory regions. Our framework formulates each analysis problem as a system of inequality constraints between symbolic bound polynomials. It then reduces the constraint system to a linear program. The solution to the linear program provides symbolic lower and upper bounds for the values of pointer and array index variables and for the regions of memory that each statement and procedure accesses. This approach eliminates fundamental problems associated with applying standard xed-point approaches to symbolic analysis problems. Experimental results from our implemented compiler show that the analysis can solve s e v eral important problems, including static race detection, automatic parallelization, static detection of array bounds violations, elimination of array bounds checks, and reduction of the number of bits used to store computed values.

We present PowerDial, a system for dynamically adapting application behavior to execute successfully in the face of load and power fluctuations. PowerDial transforms static configuration parameters into dynamic knobs that the PowerDial control system can manipulate to dynamically trade off the accuracy of the computation in return for reductions in the computational resources that the application requires to produce its results. These reductions translate directly into performance improvements and power savings.Our experimental results show that PowerDial can enable our benchmark applications to execute responsively in the face of power caps that would otherwise significantly impair responsiveness. They also show that PowerDial can significantly reduce the number of machines required to service intermittent load spikes, enabling reductions in power and capital costs.