In Partial Fulfillment of the Requirements for the Degree of Master of Science
will defend his thesis
Towards a New Directive-based Tasking API for Distributed Systems
Programming for large-scale computing requires programming models carefully designed for that purpose. MPI is often the model of choice for distributed systems, but writing MPI program is time-consuming and complicated to maintain and debug as the problem size gets bigger. Moreover, MPI does not exploit some of the potential benefits of shared memory systems. Using a hybrid model also requires a high level of programmer expertise and makes the programming task even more complex. Designing algorithms in terms of tasks rather than the specifics of threads, however, potentially reduces the development effort and has many performance-related advantages. In addition, directive-based programming styles such as OpenMP's have made parallel programming and migration of serial code to multicore chips easier than ever. Although directive-based tasking models have paved the way to distributed systems, they still lack some capabilities necessary for efficient large-scale computing.
TagHit is a directive-based API proposed by the High Performance Computing Tools group (HPCTools) in the Department of Computer Science at the University of Houston. Targeted for exascale computing, TagHit combines the benefits of task-based programming models with the simplicity of directive-based programming styles. This thesis reviews a set of related tasking models and tackles the task creation and scheduling in TagHit. First, I present an overview of six existing task-based programming models. Next, I propose an experimental runtime design of TagHit's task creation and scheduling modules and then describe in detail a prototype implementation of the runtime. The goal of this work is to guide the definition of TagHit's concept and semantics and to assess the implementation cost and challenges of creating and scheduling tasks in TagHit. Finally, I present two TagHit benchmarks with results that show that the design and implementation have proven the general concept of TagHit with good speedup and scheduling behavior.
Date: Thursday, October 6, 2016
Time: 4:00 PM
Place: PGH 501D
Advisor: Dr. Jaspal Subhlok, Dr. Barbara Chapman
Faculty, students, and the general public are invited.