College of Engineering
Department of Computer Science
Catherine Olschanowsky, Ph.D.
Scientific applications are computationally intensive and require expensive HPC resources. Optimizing scientific applications requires that we balance three competing goals: Performance, Productivity, and Portability. Performance is important because it reduces time to solution and power consumption. However, optimization has the potential to negatively impact scientific productivity due to obfuscating the code. Portable code, code that can be moved to different computers, tends to be slow and difficult to maintain. We explore using the Sparse Polyhedral Framework to create a compiler internal representation that efficiently supports optimization techniques. Automating optimizations will strike a balance among performance, productivity, and portability.
Rift, Anna; Shankar, Ravi; and Popoola, Tobi G., "SPF: Sparse Polyhedral Framework and Friends" (2021). Research Computing Days 2021. 4.