Run-time optimization of adaptive irregular applications

Run-Time Optimization of Adaptive Irregular Applications. (August 2004) Hao Yu, B.S., Tsinghua University, PR China; M.S., Tsinghua University, PR China Chair of Advisory Committee: Dr. Lawrence Rauchwerger Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploits the characteristics of input data, programs’ dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs’ input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs’ dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time, the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics of the algorithm’s input data, which is collected and analyzed dynamically. We specialized this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then