| SWIRL: High-performance many-core CPU code generation for deep neural networks A Venkat, T Rusira, R Barik, M Hall, L Truong The International Journal of High Performance Computing Applications 33 (6 …, 2019 | 34 | 2019 |
| Auto-tuning the java virtual machine S Jayasena, M Fernando, T Rusira, C Perera, C Philips 2015 IEEE International Parallel and Distributed Processing Symposium …, 2015 | 25 | 2015 |
| Predictive data locality optimization for higher-order tensor computations TR Patabandi, A Venkat, A Kulkarni, P Ratnalikar, M Hall, J Gottschlich Proceedings of the 5th ACM SIGPLAN International Symposium on Machine …, 2021 | 5 | 2021 |
| Rigel: A framework for openmp performancetuning P Rameshka, P Senanayake, T Kannangara, P Seneviratne, S Jayasena, ... 2019 IEEE 21st International Conference on High Performance Computing and …, 2019 | 5 | 2019 |
| SWIRL++ : Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks TR Patabandi, A Venkat, R Barik, M Hall International Workshop on Languages and Compilers for Parallel Computing …, 2019 | 3 | 2019 |
| Parameterized diamond tiling for parallelizing stencil computations T Wijesinghe, K Senevirathne, C Siriwardhana, W Visitha, S Jayasena, ... 2017 Moratuwa Engineering Research Conference (MERCon), 99-104, 2017 | 3 | 2017 |
| Efficiently Learning Locality Optimizations by Decomposing Transformation Domains TR Patabandi, M Hall Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler …, 2023 | 1 | 2023 |
| Automating compiler-directed autotuning for phased performance behavior T Rusira, M Hall, P Basu 2017 IEEE International Parallel and Distributed Processing Symposium …, 2017 | 1 | 2017 |
| Guiding Loop Transformations for High-Performance Tensor Applications TRKM Patabandi The University of Utah, 2022 | | 2022 |
| Optimized Code Generation for Deep Neural Networks J Lake, TR Patabandi, M Hall International Workshop on Languages and Compilers for Parallel Computing …, 2020 | | 2020 |
| Enhancing X10 performance by auto-tuning the managed java back-end V Fernando, M Fernando, T Rusira, S Jayasena 2016 Sixteenth International Conference on Advances in ICT for Emerging …, 2016 | | 2016 |
| A Novel Variable-Blocking Representation for Efficient Sparse Matrix-Vector Multiply on GPUs T Zhao, T Rusira, K Ahmad, M Hall | | |
Mary HallProfessor, School of Computing, University of UtahVerified email at cs.utah.edu
