Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse codes. Sympiler then generates specialized code or runtime schedule based on the symbolic information. The following figure shows how the input code to Sympiler is transformed internally to an optimized code. For more information, See Sympiler overview

Sympiler can be built from source code. Please look at the Getting started with Sympiler for configuration instructions. The details of the Sympiler internals are briefly explained in Sympiler docs .

Vectorizing Sparse Matrix Computations with Partially-Strided Codelets
Kazem Cheshmi, Zachary Cetinic, and Maryam Mehri Dehnavi
International Conference for High Performance Computing, Networking, Storage and Analysis, SC'22
BibTex

HDagg: Hybrid Aggregation of Loop-carried Dependence Iterations in Sparse Matrix Computations
Behrooz Zarebavani, Kazem Cheshmi, Bangtian Liu, Michelle Mills Strout, and Maryam Mehri Dehnavi
IEEE International Parallel and Distributed Processing Symposium , IPDPS'22
IEEE Xplore BibTex

Transforming Sparse Matrix Computations
Kazem Cheshmi
University of Toronto, Computer Science, PhD Thesis
BibTex

NASOQ: Numerically Accurate Sparsity-Oriented QP Solver
Kazem Cheshmi, Danny Kaufman, Shoaib Kamil, Maryam Mehri Dehnavi
SIGGRAPH 2020. NASOQ Webpage

ParSy: Inspection and Transformation of Sparse Matrix Computations for Parallelism
Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout, Maryam Mehri Dehnavi
International Conference for High Performance Computing, Networking, Storage, and Analysis, SC'18
ACM DL BibTex EndNote ACM Ref

Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout, Maryam Mehri Dehnavi
International Conference for High Performance Computing, Networking, Storage and Analysis, SC'17
DOI: https://doi.org/10.1145/3126908.3126936. BibTex EndNote ACM Ref

Decoupling Symbolic from Numeric in Sparse Matrix Computations
Kazem Cheshmi, Maryam Mehri Dehnavi
First place in 2017 ACM SRC Grand Finals Winners

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License

Sympiler is open source, under a commercially permissive MIT license. We encourage you to use it in other projects, open source or commercial!

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