Title: Transforming Computation and Communication Patterns for High-Performance

Speaker: Maryam Mehri Dehnavi, University of Toronto

Abstract: The emergence of stupendously large matrices in applications such as data mining and large-scale scientific simulations has rendered the classical software frameworks and numerical methods inadequate in many situations. In this talk, I will demonstrate how building domain-specific compilers and reformulating classical mathematical methods significantly improve the performance and scalability of large-scale applications on modern computing platforms. I will introduce Sympiler, a domain-specific code generator that transforms computation patterns in sparse matrix methods for high-performance. Specifically, I will demonstrate how decoupling symbolic analysis from numerical manipulation will enable automatic optimization of sparse codes with static sparsity patterns. The performance of Sympiler-generated code will be compared to optimized library codes to demonstrate the effectiveness of symbolic decoupling.

Biography: Maryam Mehri Dehnavi is an Assistant Professor in the Computer Science department at the University of Toronto. Her research focuses on high-performance computing and domain-specific compiler design. Previously, she was an Assistant Professor at Rutgers University, a postdoctoral researcher at MIT and a visiting scholar at UC Berkeley. She received her Ph.D. in Electrical and Computer Engineering from McGill University in 2013.

For more information, contact Aaron Babier.