Curriculum Vitae

Looplets: A Language for Structured Coiteration

W. Ahrens, D. Donenfeld, F. Kjolstad, and S. Amarasinghe, “Looplets: A Language for Structured Coiteration,” in Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization, in CGO 2023. New York, NY, USA: Association for Computing Machinery, Feb. 2023, pp. 41–54. Link. Download

Autoscheduling For Sparse Tensor Algebra With An Asymptotic Cost Model

W. Ahrens, F. Kjolstad, and S. Amarasinghe, “Autoscheduling For Sparse Tensor Algebra With An Asymptotic Cost Model,” in Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation, New York, NY, USA, Jun. 2022, pp. 269–285. Link. Download.

Contiguous Graph Partitioning For Optimal Total Or Bottleneck Communication

W. Ahrens, “Contiguous Graph Partitioning For Optimal Total Or Bottleneck Communication,” arXiv:2007.16192 [cs], Jun. 2021. Link. Download.

Algorithms for Efficient Reproducible Floating Point Summation

W. Ahrens, J. Demmel, and H. D. Nguyen, “Algorithms for Efficient Reproducible Floating Point Summation,” ACM Trans. Math. Softw., vol. 46, no. 3, p. 22:1–22:49, Jul. 2020. Link. Download.

Brief Announcement: Sparse Tensor Transpositions

S. Mueller, W. Ahrens, S. Chou, F. Kjolstad, and S. Amarasinghe, “Brief Announcement: Sparse Tensor Transpositions,” in Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 2020, pp. 559–561. Link. Download.

Sparse Tensor Transpositions

S. Mueller, W. Ahrens, S. Chou, F. Kjolstad, and S. Amarasinghe, “Sparse Tensor Transpositions,” arXiv:2005.10427 [cs], May 2020. Link. Download.

On Optimal Partitioning For Sparse Matrices In Variable Block Row Format

W. Ahrens and E. G. Boman, “On Optimal Partitioning For Sparse Matrices In Variable Block Row Format,” arXiv:2005.12414 [cs], May 2020. Link. Download.

Tensor Algebra Compilation with Workspaces

F. Kjolstad, W. Ahrens, S. Kamil, and S. Amarasinghe, “Tensor Algebra Compilation with Workspaces,” in 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), 2019, pp. 180–192. Link. Download.

A Parallel Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats

W. J. Ahrens, “A Parallel Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats,” Thesis, Massachusetts Institute of Technology, 2019. Link. Download.

LATE Ain’T Earley: A Faster Parallel Earley Parser

W. Ahrens, J. Feser, and R. Hui, “LATE Ain’T Earley: A Faster Parallel Earley Parser,” arXiv:1807.05642 [cs], Jul. 2018. Link. Download.

A Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats

W. Ahrens, H. Xu, and N. Schiefer, “A Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats,” in 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2018, pp. 546–556. Link. Download.

Efficient Reproducible Floating Point Summation and BLAS

W. Ahrens, J. Demmel, and H. D. Nguyen, “Efficient Reproducible Floating Point Summation and BLAS,” EECS Department, University of California, Berkeley, UCB/EECS-2016-121, Jun. 2016. Link. Download.

Parallel Compact Hash Algorithms for Computational Meshes

R. Tumblin, W. Ahrens, S. Hartse, and R. Robey, “Parallel Compact Hash Algorithms for Computational Meshes,” SIAM J. Sci. Comput., vol. 37, no. 1, pp. C31–C53, Jan. 2015. Link. Download.