| Software-hardware co-design for fast and scalable training of deep learning recommendation models D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ... Proceedings of the 49th Annual International Symposium on Computer …, 2022 | 172* | 2022 |
| The MVAPICH project: Transforming research into high-performance MPI library for HPC community DK Panda, H Subramoni, CH Chu, M Bayatpour Journal of Computational Science 52, 101208, 2021 | 82 | 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 | 62 | 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 | 59 | 2018 |
| 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 | 51 | 2020 |
| 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 |
| Designing high-performance mpi libraries with on-the-fly compression for modern gpu clusters Q Zhou, C Chu, NS Kumar, P Kousha, SM Ghazimirsaeed, H Subramoni, ... 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2021 | 37 | 2021 |
| High-performance, distributed training of large-scale deep learning recommendation models D Mudigere, Y Hao, J Huang, A Tulloch, S Sridharan, X Liu, M Ozdal, ... arXiv preprint arXiv:2104.05158, 2021 | 37 | 2021 |
| 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 | 32 | 2016 |
| 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 | 31 | 2015 |
| Performance evaluation of MPI libraries on GPU-enabled OpenPOWER architectures: Early experiences KS Khorassani, CH Chu, H Subramoni, DK Panda High Performance Computing: ISC High Performance 2019 International …, 2019 | 29 | 2019 |
| Improving SCTP performance by jitter-based congestion control over wired-wireless networks JM Chen, CH Chu, EHK Wu, MF Tsai, JR Wang EURASIP Journal on Wireless Communications and Networking 2011, 1-13, 2011 | 28 | 2011 |
| Designing a ROCm-aware MPI library for AMD GPUs: early experiences K Shafie Khorassani, J Hashmi, CH Chu, CC Chen, H Subramoni, ... International Conference on High Performance Computing, 118-136, 2021 | 24 | 2021 |
| Designing a profiling and visualization tool for scalable and in-depth analysis of high-performance GPU clusters P Kousha, B Ramesh, KK Suresh, CH Chu, A Jain, N Sarkauskas, ... 2019 IEEE 26th International Conference on High Performance Computing, Data …, 2019 | 24 | 2019 |
| Characterizing cuda unified memory (um)-aware mpi designs on modern gpu architectures KV Manian, AA Ammar, A Ruhela, CH Chu, H Subramoni, DK Panda Proceedings of the 12th Workshop on General Purpose Processing Using GPUs, 43-52, 2019 | 24 | 2019 |
| Efficient and scalable multi-source streaming broadcast on GPU clusters for deep learning CH Chu, X Lu, AA Awan, H Subramoni, J Hashmi, B Elton, DK Panda 2017 46th International Conference on Parallel Processing (ICPP), 161-170, 2017 | 24 | 2017 |
| Communication profiling and characterization of deep-learning workloads on clusters with high-performance interconnects AA Awan, A Jain, CH Chu, H Subramoni, DK Panda IEEE Micro 40 (1), 35-43, 2019 | 23 | 2019 |
| Optimized large-message broadcast for deep learning workloads: MPI, MPI+ NCCL, or NCCL2? AA Awan, KV Manian, CH Chu, H Subramoni, DK Panda parallel computing 85, 141-152, 2019 | 22 | 2019 |
| Better together: Jointly optimizing {ML} collective scheduling and execution planning using {SYNDICATE} K Mahajan, CH Chu, S Sridharan, A Akella 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2023 | 18 | 2023 |
| Exploiting hardware multicast and GPUDirect RDMA for efficient broadcast CH Chu, X Lu, AA Awan, H Subramoni, B Elton, DK Panda IEEE Transactions on Parallel and Distributed Systems 30 (3), 575-588, 2018 | 18 | 2018 |
Dhabaleswar K. PandaProfessor of Computer Science, The Ohio State UniversityВерификована је имејл адреса на cse.ohio-state.edu
