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Seminar: Maryam Mehri Dehnavi, Rutgers University

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ITB 201

Transforming Computation and Communication Patterns for High-Performance

Overview

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.

In the first part of the talk, 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.

In the second part of the talk, I will show that through mathematical reformulation, communication patterns in classical optimization methods can be transformed to reduce communication costs. As a result, the performance of optimization algorithms is inherently improved when executed on distributed platforms leading to significant speedups compared to the classical formulations.