| 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 | 386 | 2019 |
| The ELPA library: scalable parallel eigenvalue solutions for electronic structure theory and computational science A Marek, V Blum, R Johanni, V Havu, B Lang, T Auckenthaler, A Heinecke, ... Journal of Physics: Condensed Matter 26 (21), 213201, 2014 | 336 | 2014 |
| LIBXSMM: accelerating small matrix multiplications by runtime code generation A Heinecke, G Henry, M Hutchinson, H Pabst SC'16: Proceedings of the International Conference for High Performance …, 2016 | 224 | 2016 |
| 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 | 219 | 2018 |
| Design and implementation of the linpack benchmark for single and multi-node systems based on intel® xeon phi coprocessor A Heinecke, K Vaidyanathan, M Smelyanskiy, A Kobotov, R Dubtsov, ... 2013 IEEE 27th International Symposium on Parallel and Distributed …, 2013 | 217 | 2013 |
| Petascale high order dynamic rupture earthquake simulations on heterogeneous supercomputers A Heinecke, A Breuer, S Rettenberger, M Bader, AA Gabriel, C Pelties, ... SC'14: Proceedings of the International Conference for High Performance …, 2014 | 182 | 2014 |
| ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems C Niethammer, S Becker, M Bernreuther, M Buchholz, W Eckhardt, ... Journal of chemical theory and computation 10 (10), 4455-4464, 2014 | 171 | 2014 |
| Fp8 formats for deep learning P Micikevicius, D Stosic, N Burgess, M Cornea, P Dubey, R Grisenthwaite, ... arXiv preprint arXiv:2209.05433, 2022 | 151 | 2022 |
| Anatomy of high-performance deep learning convolutions on SIMD architectures E Georganas, S Avancha, K Banerjee, D Kalamkar, G Henry, H Pabst, ... SC18: International Conference for High Performance Computing, Networking …, 2018 | 145 | 2018 |
| 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 | 139 | 2021 |
| 591 TFLOPS multi-trillion particles simulation on SuperMUC W Eckhardt, A Heinecke, R Bader, M Brehm, N Hammer, H Huber, ... Supercomputing: 28th International Supercomputing Conference, ISC 2013 …, 2013 | 104 | 2013 |
| Sustained petascale performance of seismic simulations with SeisSol on SuperMUC A Breuer, A Heinecke, S Rettenberger, M Bader, AA Gabriel, C Pelties Supercomputing: 29th International Conference, ISC 2014, Leipzig, Germany …, 2014 | 97 | 2014 |
| From gpgpu to many-core: Nvidia fermi and intel many integrated core architecture A Heinecke, M Klemm, HJ Bungartz Computing in Science & Engineering 14 (2), 78-83, 2012 | 87 | 2012 |
| Leveraging the bfloat16 artificial intelligence datatype for higher-precision computations G Henry, PTP Tang, A Heinecke 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), 69-76, 2019 | 84 | 2019 |
| Computer processor for higher precision computations using a mixed-precision decomposition of operations G Henry, A Heinecke US Patent 10,853,067, 2020 | 71 | 2020 |
| Efficient shared-memory implementation of high-performance conjugate gradient benchmark and its application to unstructured matrices J Park, M Smelyanskiy, K Vaidyanathan, A Heinecke, DD Kalamkar, X Liu, ... SC'14: Proceedings of the International Conference for High Performance …, 2014 | 67 | 2014 |
| Methods and apparatus to detect anomalies of a monitored system M Agerstam, B Sadeghi, J Martin, J Ota, J Gottschlich, M Carranza, ... US Patent 10,802,942, 2020 | 65 | 2020 |
| Performance optimizations for scalable implicit RANS calculations with SU2 TD Economon, D Mudigere, G Bansal, A Heinecke, F Palacios, J Park, ... Computers & Fluids 129, 146-158, 2016 | 64 | 2016 |
| 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 | 56 | 2020 |
| Systems and methods for performing 16-bit floating-point matrix dot product instructions AF Heinecke, R Valentine, MJ Charney, R Sade, M Adelman, Z Sperber, ... US Patent 10,963,246, 2021 | 55 | 2021 |
