Cache Performance Optimization for Processing XML-Based Application Data on Multi-core Processors

There is a critical need to develop new programming paradigms for grid middleware tools and applications to harness the opportunities presented by emerging multi-core processors. Implementations of grid middleware and applications that do not adapt to the programming paradigm when executing on emerging processors can severely impact the overall performance. We focus on the utilization of the L2 cache, which is a critical shared resource on Chip Multiprocessors. The access pattern of the shared L2 cache, which is dependent on how the application schedules and assigns processing work to each thread, can either enhance or undermine the ability to hide memory latency on a multi-core processor. None of the current grid simulators and emulators provides feedback and fine-grained performance data that is essential for a detailed analysis. Using the feedback from an emulation framework, we present performance analysis and provide recommendations on how processing threads can be scheduled on multi-core nodes to enhance the performance of a class of grid applications that requires processing of large-scale XML data. In particular, we discuss the gains associated with the use of the adaptations we have made to the Cache-Affinity and Balanced-Set scheduling algorithms to improve L2 cache performance, and hence the overall application execution time.