| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, J Aneja, H Awadalla, A Awadallah, AA Awan, N Bach, A Bahree, ... arXiv preprint arXiv:2404.14219, 2024 | 523 | 2024 |
| Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale RY Aminabadi, S Rajbhandari, AA Awan, C Li, D Li, E Zheng, O Ruwase, ... SC22: International Conference for High Performance Computing, Networking …, 2022 | 275 | 2022 |
| Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale S Rajbhandari, C Li, Z Yao, M Zhang, RY Aminabadi, AA Awan, J Rasley, ... International conference on machine learning, 18332-18346, 2022 | 228 | 2022 |
| S-caffe: Co-designing mpi runtimes and caffe for scalable deep learning on modern gpu clusters AA Awan, K Hamidouche, JM Hashmi, DK Panda ACM PPoPP '17 52 (8), 193-205, 2017 | 180 | 2017 |
| 1-bit adam: Communication efficient large-scale training with adam’s convergence speed H Tang, S Gan, AA Awan, S Rajbhandari, C Li, X Lian, J Liu, C Zhang, ... International Conference on Machine Learning, 10118-10129, 2021 | 90 | 2021 |
| An in-depth performance characterization of CPU-and GPU-based DNN training on modern architectures AA Awan, H Subramoni, DK Panda Proceedings of the Machine Learning on HPC Environments, 1-8, 2017 | 89 | 2017 |
| Scalable and efficient moe training for multitask multilingual models YJ Kim, AA Awan, A Muzio, AFC Salinas, L Lu, A Hendy, S Rajbhandari, ... arXiv preprint arXiv:2109.10465, 2021 | 77 | 2021 |
| Scalable distributed dnn training using tensorflow and cuda-aware mpi: Characterization, designs, and performance evaluation AA Awan, J Bédorf, CH Chu, H Subramoni, DK Panda 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2019 | 61 | 2019 |
| Optimized broadcast for deep learning workloads on dense-GPU InfiniBand clusters: MPI or NCCL? AA Awan, CH Chu, H Subramoni, DK Panda Proceedings of the 25th European MPI Users' Group Meeting, 1-9, 2018 | 60 | 2018 |
| Efficient large message broadcast using NCCL and CUDA-aware MPI for deep learning AA Awan, K Hamidouche, A Venkatesh, DK Panda Proceedings of the 23rd European MPI Users' Group Meeting, 15-22, 2016 | 60 | 2016 |
| Gems: Gpu-enabled memory-aware model-parallelism system for distributed dnn training A Jain, AA Awan, AM Aljuhani, JM Hashmi, QG Anthony, H Subramoni, ... SC20: International Conference for High Performance Computing, Networking …, 2020 | 58 | 2020 |
| Deepspeed-chat: Easy, fast and affordable rlhf training of chatgpt-like models at all scales Z Yao, RY Aminabadi, O Ruwase, S Rajbhandari, X Wu, AA Awan, ... arXiv preprint arXiv:2308.01320, 2023 | 53 | 2023 |
| Nv-group: link-efficient reduction for distributed deep learning on modern dense gpu systems CH Chu, P Kousha, AA Awan, KS Khorassani, H Subramoni, DK Panda Proceedings of the 34th ACM International Conference on Supercomputing, 1-12, 2020 | 49 | 2020 |
| Performance characterization of dnn training using tensorflow and pytorch on modern clusters A Jain, AA Awan, Q Anthony, H Subramoni, DKDK Panda 2019 IEEE International Conference on Cluster Computing (CLUSTER), 1-11, 2019 | 47 | 2019 |
| Privacy-aware searching with oblivious term matching for cloud storage Z Pervez, AA Awan, AM Khattak, S Lee, EN Huh The Journal of Supercomputing 63, 538-560, 2013 | 47 | 2013 |
| Oc-dnn: Exploiting advanced unified memory capabilities in cuda 9 and volta gpus for out-of-core dnn training AA Awan, CH Chu, H Subramoni, X Lu, DK Panda 2018 IEEE 25th International Conference on High Performance Computing (HiPC …, 2018 | 41 | 2018 |
| 1-bit LAMB: communication efficient large-scale large-batch training with LAMB’s convergence speed C Li, AA Awan, H Tang, S Rajbhandari, Y He 2022 IEEE 29th International Conference on High Performance Computing, Data …, 2022 | 34 | 2022 |
| Scaling tensorflow, pytorch, and mxnet using mvapich2 for high-performance deep learning on frontera A Jain, AA Awan, H Subramoni, DK Panda 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 76-83, 2019 | 30 | 2019 |
| Exploiting GPUDirect RDMA in designing high performance OpenSHMEM for NVIDIA GPU clusters K Hamidouche, A Venkatesh, AA Awan, H Subramoni, CH Chu, ... 2015 IEEE International Conference on Cluster Computing, 78-87, 2015 | 30 | 2015 |
| Cuda kernel based collective reduction operations on large-scale gpu clusters CH Chu, K Hamidouche, A Venkatesh, AA Awan, DK Panda 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid …, 2016 | 29 | 2016 |
Dhabaleswar K. PandaProfessor of Computer Science, The Ohio State UniversityVerified email at cse.ohio-state.edu
