creator Yin, Yanlong advisor Sun, Xian-He Many High-Performance Computing (HPC) applications spend a significant portion of their execution time in accessing data from les and they are becoming increasingly data-intensive. For them, I/O performance is a significant bottleneck leading to wastage of CPU cycles and the corresponding wasted energy consumption. Various optimization techniques exist to improve data access performance. However, the existing general-purpose optimization techniques are not able to satisfy diverse applications' demands. On the other hand, the application-specific optimization pro- cess is usually a difficult task due to the complexity involved in understanding the parallel I/O system and the applications' I/O behaviors. To address these challenges, this thesis proposes an application-aware data access optimization framework and claims that it is feasible and useful to utilize applications' characteristics to improve the performance and efficiency of the parallel I/O system. Under this framework, an optimization may consist of several basic but challenging steps, including capturing the application's characteristics, identifying the causality of I/O performance degra- dation, and delivering optimization solutions. To make these steps easier, we design and implement the IOSIG toolkit as an essential system support for the default par- allel I/O system. The toolkit is able to pro le the applications' I/O behaviors and then generate comprehensive characteristics through trace analysis. With the help of IOSIG, we design several optimization techniques on data layout optimization, data reorganization, and I/O scheduling. The proposed framework has significant poten- tial to boost application-aware I/O optimization. The results prove that the proposed optimization techniques can significantly improve the data access performance. Submitted by Erma Thomas (thomase@iit.edu) on 2015-05-14T16:39:09Z No. of bitstreams: 2 YIN_YANLONG_THESIS.pdf: 3202360 bytes, checksum: 12b3a999c5cbb0556d183ce4a8000907 (MD5) YIN_YANLONG_THESIS_TITLE_PAGE.pdf: 970196 bytes, checksum: 193b94d84c51bdd4615806e97236f1c1 (MD5) Made available in DSpace on 2015-05-14T16:39:09Z (GMT). No. of bitstreams: 2 YIN_YANLONG_THESIS.pdf: 3202360 bytes, checksum: 12b3a999c5cbb0556d183ce4a8000907 (MD5) YIN_YANLONG_THESIS_TITLE_PAGE.pdf: 970196 bytes, checksum: 193b94d84c51bdd4615806e97236f1c1 (MD5) Previous issue date: 2014-07 Ph.D. in Computer Science, July 2014 2014 2014-07 http://hdl.handle.net/10560/3410 en Dissertation born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access CS / Computer Science Illinois Institute of Technology Affiliated department