An experimental evaluation of continuous testing during development
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Proceedings of the 27th international conference on Software engineering
Distributed performance testing using statistical modeling
A-MOST '05 Proceedings of the 1st international workshop on Advances in model-based testing
Multiple Page Size Modeling and Optimization
Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques
Online performance auditing: using hot optimizations without getting burned
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Performance and environment monitoring for continuous program optimization
IBM Journal of Research and Development
IEEE Transactions on Software Engineering
Modeling Relations between Inputs and Dynamic Behavior for General Programs
Languages and Compilers for Parallel Computing
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Information and Software Technology
Exploiting statistical correlations for proactive prediction of program behaviors
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
An input-centric paradigm for program dynamic optimizations
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
A step towards transparent integration of input-consciousness into dynamic program optimizations
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
JIT technology with C/C++: Feedback-directed dynamic recompilation for statically compiled languages
ACM Transactions on Architecture and Code Optimization (TACO)
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Over the past several decades, the compiler research community has developed a number of sophisticated and powerful algorithms for a variety of code improvements. While there are still promising directions for particular optimizations, research on new or improved optimizations is reaching the point of diminishing returns and new approaches are needed to achieve significant performance improvements beyond traditional optimizations. In this paper, we describe a new strategy based on a continuous compilation system that constantly improves application code by applying aggressive and adaptive code optimizations at all times, from static optimization to online dynamic optimization. In this paper, we describe our general approach and process for continuous compilation of application code. We also present initial results from our research with continuous compilation. These initial results include a new prediction framework that can estimate the benefit of applying code transformations without actually doing the transformation. We also describe results that demonstrate the benefit of adaptively changing application code for embedded systems to make trade-offs between code size, performance, and power consumption.