| A study of BFLOAT16 for deep learning training D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ... arXiv preprint arXiv:1905.12322, 2019 | 331 | 2019 |
| A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome JA Chapman, M Mascher, A Buluç, K Barry, E Georganas, A Session, ... Genome biology 16, 1-17, 2015 | 307 | 2015 |
| Critical assessment of metagenome interpretation: the second round of challenges F Meyer, A Fritz, ZL Deng, D Koslicki, TR Lesker, A Gurevich, G Robertson, ... Nature methods 19 (4), 429-440, 2022 | 222 | 2022 |
| Mixed precision training of convolutional neural networks using integer operations D Das, N Mellempudi, D Mudigere, D Kalamkar, S Avancha, K Banerjee, ... arXiv preprint arXiv:1802.00930, 2018 | 195 | 2018 |
| Anatomy of high-performance deep learning convolutions on SIMD architectures E Georganas, S Avancha, K Banerjee, D Kalamkar, G Henry, H Pabst, ... SC '18 Proceedings of the International Conference for High Performance …, 0 | 135* | |
| Distgnn: Scalable distributed training for large-scale graph neural networks V Md, S Misra, G Ma, R Mohanty, E Georganas, A Heinecke, D Kalamkar, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 113 | 2021 |
| Parallel de bruijn graph construction and traversal for de novo genome assembly E Georganas, A Buluç, J Chapman, L Oliker, D Rokhsar, K Yelick SC'14: Proceedings of the International Conference for High Performance …, 2014 | 109 | 2014 |
| HipMer: An extreme-scale de novo genome assembler E Georganas, A Buluç, J Chapman, S Hofmeyr, C Aluru, R Egan, L Oliker, ... International Conference for High Performance Computing, Networking, Storage …, 2015 | 97 | 2015 |
| merAligner: A Fully Parallel Sequence Aligner E Georganas, A Buluc, J Chapman, L Oliker, D Rokhsar, K Yelick Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE …, 2015 | 61 | 2015 |
| Communication avoiding and overlapping for numerical linear algebra E Georganas, J González-Domínguez, E Solomonik, Y Zheng, J Tourino, ... SC'12: Proceedings of the International Conference on High Performance …, 2012 | 56 | 2012 |
| A communication-optimal n-body algorithm for direct interactions M Driscoll, E Georganas, P Koanantakool, E Solomonik, K Yelick 2013 IEEE 27th International Symposium on Parallel and Distributed …, 2013 | 52 | 2013 |
| Terabase-scale metagenome coassembly with MetaHipMer S Hofmeyr, R Egan, E Georganas, AC Copeland, R Riley, A Clum, ... Scientific reports 10 (1), 10689, 2020 | 50 | 2020 |
| Optimizing deep learning recommender systems training on cpu cluster architectures D Kalamkar, E Georganas, S Srinivasan, J Chen, M Shiryaev, A Heinecke SC20: International Conference for High Performance Computing, Networking …, 2020 | 46 | 2020 |
| Extreme scale de novo metagenome assembly E Georganas, R Egan, S Hofmeyr, E Goltsman, B Arndt, A Tritt, A Buluc, ... SC '18 Proceedings of the International Conference for High Performance …, 0 | 46* | |
| Harnessing deep learning via a single building block E Georganas, K Banerjee, D Kalamkar, S Avancha, A Venkat, ... 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020 | 24 | 2020 |
| The parallelism motifs of genomic data analysis K Yelick, A Buluç, M Awan, A Azad, B Brock, R Egan, S Ekanayake, ... Philosophical Transactions of the Royal Society A 378 (2166), 20190394, 2020 | 22 | 2020 |
| Tensor processing primitives: A programming abstraction for efficiency and portability in deep learning workloads E Georganas, D Kalamkar, S Avancha, M Adelman, C Anderson, A Breuer, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 20 | 2021 |
| Efficient and generic 1d dilated convolution layer for deep learning N Chaudhary, S Misra, D Kalamkar, A Heinecke, E Georganas, B Ziv, ... arXiv preprint arXiv:2104.08002, 2021 | 17 | 2021 |
| Scalable parallel algorithms for genome analysis E Georganas University of California, Berkeley, 2016 | 12 | 2016 |
| FPGA-based AI smart NICs for scalable distributed AI training systems R Ma, E Georganas, A Heinecke, S Gribok, A Boutros, E Nurvitadhi IEEE Computer Architecture Letters 21 (2), 49-52, 2022 | 11 | 2022 |
Alexander HeineckeIntel Fellow at Intel Labs在 intel.com 的电子邮件经过验证
