Whereas traditional high-performance computing (HPC) applications are computationally intensive, recent HPC applications require more data-intensive analysis and visualization to extract knowledge. In many cases, these applications execute the same computational algorithm as in the past (e.g., parallel search or parallel rendering) but now must do so for significantly larger data sets. For example, the life sciences, along with the cross-cutting area of scientific visualization, constitute an emerging category of HPC applications that not only perform sophisticated calculations but also ingest a sea of data. Running these new HPC data-parallel applications on today's computing platforms imposes new challenges and demands additional functionality.
However, today's HPC platforms still adopt a compute-centric model and do not handle these new challenges well. Such a model often moves a large amount of data to various parallel computational processes. Consequently, long CPU wait times for I/O to complete and enormous data-movement overhead become major stumbling blocks to high performance and scalability. This project encompasses the creation of a scalable cross-layer software framework to enable both computationally intensive and data-intensive parallel HPC applications to run on distributed file systems. This framework consists of two interwoven research tasks: (1) an adaptive, data locality-aware, middleware system that dynamically schedules compute processes to access local data by monitoring physical data locations and (2) a framework that captures the computation and data I/O processing relationship from parallel applications and coordinates the scheduling of the corresponding process and I/O execution for maximum parallel efficiency. The success of this project contributes enhanced productivity and return on investment on HPC resources via the elimination of both CPU wait time and network transfer of frequently accessed data in scientific applications. An open-source, sustainable, and reusable software framework is delivered to speed-up the discovery and innovation process in areas such as bioinformatics, climate, high-energy physics, cosmology, astrophysics, and chromodynamics. The synergy in the two proposing institutions, Virginia Tech and the University of Central Florida, and their collaborating DOE national laboratories, will catalyze new and beneficial perspectives in the graduate education of students and prepare a 21st-century workforce in HPC.
