| Deep learning for computational chemistry GB Goh, NO Hodas, A Vishnu Journal of computational chemistry 38 (16), 1291-1307, 2017 | 821 | 2017 |
| NWChem: Past, present, and future E Apra, EJ Bylaska, WA De Jong, N Govind, K Kowalski, TP Straatsma, ... The Journal of chemical physics 152 (18), 2020 | 554 | 2020 |
| A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems A Hameed, A Khoshkbarforoushha, R Ranjan, PP Jayaraman, J Kolodziej, ... Computing 98, 751-774, 2016 | 445 | 2016 |
| An overview of energy efficiency techniques in cluster computing systems GL Valentini, W Lassonde, SU Khan, N Min-Allah, SA Madani, J Li, ... Cluster Computing 16, 3-15, 2013 | 290 | 2013 |
| Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models GB Goh, C Siegel, A Vishnu, NO Hodas, N Baker arXiv preprint arXiv:1706.06689, 2017 | 249 | 2017 |
| NWChem E Apra, EJ Bylaska, WA de Jong, N Govind, K Kowalski, TP Straatsma, ... American Institute of Physics, 2020 | 215 | 2020 |
| GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent J Daily, A Vishnu, T Warfel, V Amatya https://arxiv.org/pdf/1803.05880.pdf, 2018 | 207* | 2018 |
| A survey on resource allocation in high performance distributed computing systems H Hussain, SUR Malik, A Hameed, SU Khan, G Bickler, N Min-Allah, ... Parallel Computing 39 (11), 709-736, 2013 | 198 | 2013 |
| Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties GB Goh, NO Hodas, C Siegel, A Vishnu arXiv preprint arXiv:1712.02034, 2017 | 164 | 2017 |
| CFDNet: A deep learning-based accelerator for fluid simulations O Obiols-Sales, A Vishnu, N Malaya, A Chandramowliswharan Proceedings of the 34th ACM international conference on supercomputing, 1-12, 2020 | 140 | 2020 |
| Designing topology-aware collective communication algorithms for large scale infiniband clusters: Case studies with scatter and gather K Kandalla, H Subramoni, A Vishnu, DK Panda 2010 IEEE International Symposium on Parallel & Distributed Processing …, 2010 | 108 | 2010 |
| Desh: deep learning for system health prediction of lead times to failure in hpc A Das, F Mueller, C Siegel, A Vishnu Proceedings of the 27th international symposium on high-performance parallel …, 2018 | 107 | 2018 |
| Building multirail infiniband clusters: Mpi-level design and performance evaluation J Liu, A Vishnu, DK Panda SC'04: Proceedings of the 2004 ACM/IEEE conference on Supercomputing, 33-33, 2004 | 98 | 2004 |
| Using rule-based labels for weak supervised learning: a ChemNet for transferable chemical property prediction GB Goh, C Siegel, A Vishnu, N Hodas Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 93 | 2018 |
| Performance analysis of data intensive cloud systems based on data management and replication: a survey SUR Malik, SU Khan, SJ Ewen, N Tziritas, J Kolodziej, AY Zomaya, ... Distributed and Parallel Databases 34, 179-215, 2016 | 72 | 2016 |
| Iso-energy-efficiency: An approach to power-constrained parallel computation S Song, CY Su, R Ge, A Vishnu, KW Cameron 2011 IEEE International Parallel & Distributed Processing Symposium, 128-139, 2011 | 72 | 2011 |
| Distributed tensorflow with MPI A Vishnu, C Siegel, J Daily arXiv preprint arXiv:1603.02339, 2016 | 70 | 2016 |
| Kleio: A hybrid memory page scheduler with machine intelligence TD Doudali, S Blagodurov, A Vishnu, S Gurumurthi, A Gavrilovska Proceedings of the 28th International Symposium on High-Performance Parallel …, 2019 | 69 | 2019 |
| How much chemistry does a deep neural network need to know to make accurate predictions? GB Goh, C Siegel, A Vishnu, N Hodas, N Baker 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1340-1349, 2018 | 66 | 2018 |
| Hot-spot avoidance with multi-pathing over infiniband: An mpi perspective A Vishnu, M Koop, A Moody, AR Mamidala, S Narravula, DK Panda Seventh IEEE International Symposium on Cluster Computing and the Grid …, 2007 | 58 | 2007 |
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