AbstractsComputer Science

I/O is a major time-limiting factor in high performance computing (HPC) applications. The combined effects of hard drive latency and bandwidth make I/O the slowest operation in a system. A lot of work has been done in the field of parallel I/O for scientific computing, specially for distributed memory machines. As shared memory systems gain popularity with the increasing number of cores in a node, implementing efficient parallel I/O for shared memory machines has become an important challenge. Currently, popular shared memory programming models like OpenMP do not provide a framework for implementing parallel I/O. This thesis provides a parallel I/O specification for shared memory architecture. In particular, focus has been laid on implementing parallel I/O for OpenMP. In the process, the characteristics of shared memory machines and the behavior of parallel file systems have been studied and an effort has been made to optimize parallel I/O. Also, this research provides insights into semantic analysis of data using the HDF5 technology suite.