In ParaMathics, we develop novel systems, compilers, and mathematical methods that significantly improve the performance of large-scale and big data applications on parallel and cloud platforms. We leverage information from the domain and the problem structure to invent frameworks that transform the space of optimization that compilers and systems can explore. Our frameworks are designed to optimize a large class of algorithms and numerical kernels and thus support simulations of numerous applications such as in computer graphics, physics, vision, and machine learning.  Learn more


Contact: Department of Computer Science, University of Toronto, Pratt 398A, 6 King’s College Rd, Toronto, ON M5S 3G4, Canada


Multiple MSc, PhD, and Postdoctoral positions available.
Kazem receives the 2020 ACM-IEEE CS George Michael Memorial HPC Fellowship.
NASOQ is accepted in Siggraph20.
ASYNC is accepted at IPDPS2020.
MatRox is accepted at PPoPP2020.
Dave-QN is accepted at AISTATS2020.
Maryam Dehnavi is appointed as the Canada Research Chair in Parallel and Distributed Computing.
“Sparse Computation Data Dependence Simplification for Efficient Compiler-Generated Inspectors” accepted at PLDI19. Paper
Our ASYNC framework supports asynchronous machine learning on the cloud! Paper
Read about our work at UofT News.
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