This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
With the growth in the size of scientific applications the level of parallelism provided by existing techniques leads to suboptimal performance and, therefore, an integrated approach to parallelism is necessary. This research involves the acquisition of a cluster for high performance computing. Using this infrastructure the investigators will improve parallelism at many levels ranging from compiler to I/O, in order to increase the execution performance of scientific applications. The infrastructure offers a valuable tool to validate the results on a state-of-the-art system using large scale scientific applications.
This research aims to elevate the productivity and efficiency of high-performance scientific computing through innovative language, compiler, empirical tuning, parallel I/O, and power management technologies. The investigators develop new methods for flow-sensitive static and dynamic program analysis to enhance loop parallelization. The investigators implement new speculative parallelization techniques to expose higher levels of thread parallelism for chip-multiprocessors (CMPs). Furthermore, the investigators plan to build an integrated framework for parallel I/O by studing various aspects of declustering, including novel declustering schemes, replicated declustering, heterogeneous declustering, adaptive declustering and declustering using multiple databases. Finally, the investigators plan to develop efficient energy management schemes for parallel high-performance clusters, study various fault tolerance approaches by exploring the inherent space redundancy in CMPs, and address the potential negative effects of energy management on system reliability. The computing platform enables the investigators to validate the impact of their research on application performance and scalability on a large scale parallel system.
