Tera-scale high-performance computing has enabled scientists to tackle very large and computationally challenging scientific problems, making the advancement of scientific discovery at a faster pace. However, as computing scales to levels never seen before, it also becomes extremely data intensive, I/O intensive, and energy consuming. Amongst these, I/O is becoming a major bottleneck, impeding the expected pace of scientific discovery and analysis of data. Furthermore, the applications are becoming increasingly dynamic in terms of their computation patterns as well as data access patterns to cope with larger problems and data sizes. Due to the complexities of systems and applications and their high energy consumptions, it is, therefore, very important to address research issues and develop dynamic techniques at the level of run-time systems and compilers to scale I/O in the right proportions. This paper presents the details of a dynamic compilation framework developed specifically for I/O-intensive large-scale applications. Our dynamic compilation framework includes a set of powerful I/O optimizations designed to minimize execution cycles and energy consumption, and generates results that are competitive with hand-optimized codes in terms of energy consumption.
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