Please note: This seminar will be given online.
Kazem Cheshmi, Department of Computer Science
University of Toronto
Sparse matrix computations are an important class of algorithms frequently used in scientific simulations such as computer graphics and weather modeling as well as in data analytics codes and machine learning computations. The performance of these simulations relies heavily on the high-efficient implementations of sparse computations.
In this talk, I answer the fundamental question of how to optimize complex sparse codes and algorithms in real-world applications. I will introduce Sympiler, a new solution to automating and redesigning sparse computations. I will describe how Sympiler decouples symbolic information in sparse computation and automates the optimization of sparse linear algebra kernels. I will demonstrate how the proposed solutions in symbolic decoupling can automatically generate high-performance code that significantly outperforms high-optimized code from state-of-the-art libraries.
To join this seminar on MS teams, please go to https://teams.microsoft.com/l/meetup-join/19%3a5b3f97edf4634aeaa4cdf01fee9531f4%40thread.tacv2/1622830486768?context=%7b%22Tid%22%3a%22723a5a87-f39a-4a22-9247-3fc240c01396%22%2c%22Oid%22%3a%2212e14737-4261-4547-9210-71e11bd5a87b%22%7d.
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